INTEGRATED ASSESSMENT OF FREE DRAINING BASE AND SUBBASE
MATERIALS UNDER FLEXIBLE PAVEMENT
A Dissertation
Presented to
The Graduate Faculty of The University of Akron
In Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy
Samer Rateb Rabab’ah
December, 2007 INTEGRATED ASSESSMENT OF FREE DRAINING BASE AND SUBBASE
MATERIALS UNDER FLEXIBLE PAVEMENT
Samer Rateb Rabab’ah
Dissertation
Approved: Accepted:
Advisor Department Chair Dr. Robert Liang Dr. Wieslaw Binienda
Committee Member Dean of the College Dr. Alaa Abbas Dr. George K. Haritos
Committee Member Dean of the Graduate School Dr. Ping Yi Dr. George R. Newkome
Committee Member Date Dr. Chien-Chung Chan
Committee Member Dr. Yueh-Jaw Lin
ii ABSTRACT
Providing adequate drainage to a pavement system has been considered as an important design consideration to prevent premature failures due to water related problems such as pumping action, loss of support, and rutting, among others. As a result, permeable bases with drainage efficiency and structural stability characteristics have been widely used by several state DOTs in the design and construction of pavements. The
Ohio Department of Transportation (ODOT) has adopted several types of materials specification for use as permeable bases: (a) unbounded base materials including: 307-IA,
307-NJ, and 307-CE types, (b) stabilized base materials including: cement and asphalt
treated base types. The main objective of this research is to study the performance of different permeable base materials used by the Ohio Department of Transportation
(ODOT). Field monitored data combined with laboratory result data are used to determine the drainage efficiency of different permeable base materials used by ODOT.
Analysis using Multi-Layer Linear Elastic Analysis (MLEA) and data obtained from field backcalculated resilient modulus are used to predict pavement service life for pavement section with different permeable base materials.
To account for effect of environmental factors in pavement design, the Enhanced
Integrated Climatic Model was used to predict temperature, moisture and frost depth data at the Project Site, Ohio. Comparisons were made between the predicted and measured
iii moisture contents and temperature along the depth of pavement sections as well as frost
depth at different times during the simulation period.
Analysis was conducted to study the characterization of permeable base materials in
the new Mechanistic-Empirical Pavement Design Guide (MEPDG). Flexible pavement
designs and performance derived from the MEPDG approach are compared for different
base materials over a range of asphalt concrete layer thicknesses, base materials,
subgrade and other material properties. As shown consistently in this study, the impact of
unbound materials layer on performance as predicted by the MEPDG methodology is less pronounced than the impact of asphalt concrete layer.
Based on the concept of effective stress of unsaturated soils, the matric suction of
soil was shown to be an important state variable for predicting moisture-dependent
resilient modulus of cohesive soils. A prediction model is proposed and shown to be capable of predicting the resilient modulus of cohesive soils over a range of stress states and water content. The accuracy of the proposed model is validated against experimental data of A-4 and A-6 soils as well as other data available in the literature. Movement of water through the different pavement sections at the project site was modeled by the finite element method (FEM). FEM modeling taking into consideration of both saturated and unsaturated flow appears to be capable of predicting water content regime in pavement layers (base, subbase, and subgrade), as demonstrated in this study when comparing with field moisture monitoring results. Accurate simulation of the boundary and initial conditions, together with representative soil water characteristic curves and the hydraulic conductivity curves for each material, resulted in accurate predictions that match with the measured field results.
iv DEDICATION
To my father and mother, and my lovely wife Fatima who made all of this possible, for their endless encouragement and patience.
v ACKNOWLEDGMENT
I would like to express my deep appreciation to my advisor, Professor Robert Liang, for
his inspiration, motivation, guidance, and generous support throughout the past three and
a half years. I have learned so much from his keen insight, his research and problem
solving abilities, and his amazing energy. What I learned from him will be an invaluable
benefit for the rest of my life.
Thanks in large part to the kindness and mentoring provided by the committee members:
Dr. Alaa Abbas, Dr. Ping Yi , Dr. Yueh-Jaw Lin, and Dr. Chien-Chung Chan. Their
thoughtful comments and suggestions are very helpful and are greatly appreciated.
My wife, parents, and brothers have been extremely supportive of my studies, and have
always been encouraging and understanding. I am also grateful to my wonderful friends
in Akron, Ohio, and Jordan. Their aid has been invaluable during the period of this work.
vi
TABLE OF CONTENTS
Page LIST OF TABLES ..…………………………………………………………………… xiv
LIST OF FIGURES ………………………………………………………………… ... xvi
CHAPTER
I.INTRODUCTION...... 1
1.1 Statement of the Problem ...... 1
1.2 Objectives of the Study ...... 6
1.3 Organization of Dissertation ...... 9
II.BACKGROUND AND LITERATURE REVIEW...... 11
2.1 Background ...... 11
2.2 General Design Considerations for Combating Moisture ...... 14
2.2.1 Prevent Moisture from Entering the Pavement System ...... 14
2.2.2 Provide Moisture-Insensitive Materials ...... 15
2.2.2.1 Cement-Treated Base...... 15
2.2.2.2 Asphalt-Treated Base...... 15
2.2.2.3 Open Graded Base Materials ...... 16
2.2.3 Incorporate Design Features to Minimize Moisture Damage ...... 16
2.2.4 Removal of Free Moisture through Subsurface Drainage...... 17
2.3 Subsurface Drainage Terminology...... 17
2.3.1 The Decision to Include Sub-Surface Drainage...... 18
2.3.2 Drainage components or elements ...... 19 vii
2.4 Effects of Weather-Related Factors on Pavement Performance ...... 21
2.4.1 Effect of Water Content on Resilient Modulus of Subgrade Soil ...... 21
2.4.1.1 Resilient Modulus Models ...... 23
2.4.2 Models for Estimating the Resilient Modulus Based on Single Soil Parameter ...... 26
2.4.2.1 Moisture Effects on Unbound Materials...... 28
2.4.3 Temperature Effects on Soil Resilient Modulus ...... 31
2.5 Pavement Evaluation by Non-Destructive Measurements...... 32
2.6 Calibration and Validation of the Enhanced Integrated Climatic Model (EICM) ... 35
2.6.1 Importance of Climate in Mechanistic-Empirical Design...... 36
2.6.2 The Enhanced Integrated Climatic Model...... 38
2.6.3 Incorporation of EICM into the Design Guide...... 39
2.6.3.1 CMS Model...... 39
2.6.3.2 CRREL model...... 40
2.6.3.3 ID Model ...... 40
2.7 Evaluation of Mechanistic Empirical Design approach over permeable base materials...... 42
2.7.1 Inputs level ...... 45
2.7.2 Material Characterization...... 46
2.7.2.1 Asphalt Concrete Materials Characterization ...... 46
2.7.2.2 Chemically Stabilized Materials Characterization...... 47
2.7.2.3 Unbound Granular Materials and Subgrade Materials Characterization...... 47
2.7.3 MEPDG Performance Models ...... 48
2.7.3.1 Fatigue Cracking ...... 48
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2.7.3.2 Thermal Cracking of AC Layer ...... 49
2.7.3.3 Permanent Deformation ...... 50
2.7.3.4 Estimating Pavement Smoothness (IRI) ...... 51
2.8 Evaluation of Water Flow within Pavement System...... 51
2.8.1 Current State-of-Practice...... 52
2.8.2 Hydraulic Design of Sub-Surface Drainage...... 52
2.8.3 Design of Subsurface Drainage using Unsaturated Soil Principles ...... 55
2.8.3.1 Unsaturated Flow through Pavements ...... 56
2.8.3.2 Soil Suction...... 57
2.8.3.3 Soil Water Characteristic Curve (SWCC)...... 58
2.8.3.4 Hydraulic Conductivity Curve Models ...... 61
2.8.3.5 Finite Element Analysis of Pavement Drainage ...... 61
III. ATB-90 RESEARCH PROJECT ...... 64
3.1 Overview of Work Performed...... 64
3.2 Research Program...... 66
3.2.1 Instrumentation Layout ...... 66
3.2.2 Material Specifications...... 69
3.2.3 Structural Stability of Permeable Base Materials...... 70
3.3 Project Summary and Observations ...... 72
3.3.1 Summary of Laboratory Work ...... 73
3.3.2 Summary of Field Monitoring Work ...... 76
3.3.3 Non-Destructive Testing ...... 77
3.4 Summary and Conclusions...... 79
3.4.1 Saturation in Subgrade ...... 79
3.4.2 Drainage Efficiency...... 80 ix
3.4.3 Structural Stability Characteristics...... 80
3.4.3.1 Resilient Modulus ...... 81
3.4.3.2 Permanent Deformation ...... 81
3.4.3.3 Durability ...... 82
3.4.4 Long-Term Performance Evaluation...... 82
3.4.4.1 FWD Test...... 82
3.4.4.2 International Roughness Index (IRI)...... 83
IV. EFFECTS OF USING PERMEABLE BASE LAYER ON THE SUBGRADE MOISTURE REGIME AND THE OVERALL PAVEMENT PERFORMANCE...... 84
4.1 Introduction ...... 85
4.2 Characterizing moisture in MEPDG ...... 87
4.3 Analysis of Collected Moisture Data ...... 88
4.3.1 Variation of Moisture Content from Initial Water Content...... 99
4.3.2 Variation of Moisture Content from Avg. water content ...... 104
4.3.3 Variation of Moisture Content with Depth from Season to Season...... 107
4.4 Impact of Permeable Base Materials on the Pavement Structural Capacity ...... 122
4.5 Comparison of Field Backcalculated and Laboratory Determined Moduli ...... 123
4.6 Estimation of Subgrade Seasonal Adjustment Factor (SAF) ...... 124
4.7 Multilayer Elastic Analysis ...... 126
4.8 Prediction of Pavement Service Life...... 128
4.9 Conclusions ...... 131
V. EVALUATION OF ENHANCED INTEGRATED CLIMATIC MODEL PREDICTION OVER DIFFERENT PERMEABLE BASE MATERIALS...... 133
5.1 Importance of Climate in Mechanistic-Empirical Design...... 133
5.2 The Enhanced Integrated Climatic Model (EICM)...... 135
x
5.2.1 Incorporation of EICM into the Design Guide...... 136
5.3 Using TDR to Measure Moisture Content and Dry Density...... 138
5.4 Evaluation Results...... 142
5.4.1 Temperature Data Comparison ...... 142
5.4.2 Frost Depth Comparison ...... 149
5.4.3 Moisture Data Comparison ...... 151
5.5 Sensitivity Analysis for Temperature Predictions...... 154
5.6 Summary and Conclusions...... 157
VI. PREDICTING MOISTURE-DEPENDENT RESILIENT MODULUS OF COHESIVE SOILS USING SOIL SUCTION CONCEPT...... 159
6.1 Introduction ...... 160
6.2 Resilient Modulus Models...... 161
6.3 Previous Models Incorporating Soil Suction...... 164
6.4 Proposed Model Incorporating Soil Suction Concept...... 165
6.5 Soil Suction ...... 167
6.5.1 Soil Suction Concept...... 167
6.5.2 Soil Water Characteristic Curve (SWCC)...... 168
6.6 Validation of the Proposed Model...... 170
6.7 Summary of the Proposed Procedure ...... 177
6.8 Summary and Conclusions...... 178
VII. EVALUATION OF MECHANISTIC EMPIRICAL DESIGN APPROACH OVER PERMEABLE BASE MATERIALS...... 179
7.1 Introduction ...... 179
7.2 Characterization of Base and Subgrade Materials ...... 181
7.2.1 Resilient Modulus-Level 1 design: Laboratory testing ...... 182
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7.2.2 Resilient Modulus-Level 2 design: Correlations with other material properties...... 183
7.2.3 Resilient Modulus-Level 3 design: Typical Values ...... 183
7.2.4 Resilient Modulus as Function of Soil Moisture...... 184
7.3 M-E Design Guide Performance Models ...... 186
7.4 Resilient modulus test results...... 186
7.5 Procedure Implementation ...... 187
7.5 Results and Analysis ...... 191
7.6 Pavement Service Life...... 199
7.7 Summary and Conclusions...... 202
VIII. FINITE ELEMENT MODELING OF FIELD PERFORMANCE OF DIFFERENT PERMEABLE BASE UNDER ASPHALT PAVEMENT ...... 204
8.1 Background ...... 205
8.2 Hydraulic Design of Sub-Surface Drainage...... 206
8.3 Finite Element (FE) Modeling ...... 208
8.3.1 Boundary Conditions...... 212
8.3.2 Analysis Procedure...... 213
8.3.4 Model Calibration Using Field Data ...... 214
8.4 Parametric Study ...... 219
8.4.1 Effect of Precipitation Infiltration Rate...... 219
8.4.2 Influence of Water Table...... 220
8.4.3 Drainage Efficiency of Permeable Base...... 222
8.5 Design Consideration of Permeable Base Layer...... 226
8.5.1 Effect of Hydraulic Conductivity of Base Course Materials ...... 228
8.5.2 Effect of Permeable Base Layer Thickness...... 230
xii
8.5.3 Effect of Subbase Hydraulic Conductivity...... 231
8.5.4 Pavement with No Underdrain Pipes ...... 232
8.5.5 Effect of Pavement Shoulder Slope ...... 235
8.6 Summary and Conclusions...... 236
IX. CONCLUSIONS AND RECOMENDATIONS...... 239
9.1 Summary ...... 239
9.2 Conclusions ...... 242
9.3 Recommendations for Future Study...... 246
REFERENCES...... 248
xiii
LIST OF TABLES
Table Page
Table 2.1 Permeable base quality of drainage rating based on time taken to drain...... 53
Table 3.1 Comparison of Permeability of different permeable base materials...... 74
Table 3.2 Accumulation of permanent strains of different materials at different stress
ratio after 40,000 load cycles...... 76
Table 4.1 Monthly moisture COV exceeded the mean COV ...... 98
Table 4.2 Percent change in seasonal moisture content from initial moisture content..... 99
Table 4.3 Date to reach equilibrium moisture content...... 103
Table 4.4 Percent variation in moisture content from average moisture content for
different seasons ...... 107
Table 4.5 Percent seasonal variation of moisture content from mean moisture content
for year 2004...... 108
Table 4.6 Percent seasonal variation of moisture content from mean moisture content
for year 2005...... 109
Table 4.7 Laboratory and EverCalc backcalculations comparison for pavement layer
moduli (ksi) for both sites...... 124
Table 4.8 subgrade monthly adjustment factor...... 125
Table 4.9 subgrade seasonal adjustment factor...... 125
Table 5.1 Properties of Permeable Bases Materials used in the model...... 140
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Table 5.2 Field dry density results for 307-NJ section, driving lane, at Phase II site. ... 153
Table 6.1 Values of a, b, and k for coarse-grained and fine-grained materials m
(MEPDG, 2004) ...... 163
Table 6.2 Summary of the particle-size analysis, Atterberg’s limits, and compaction
properties for cohesive soils in this study...... 170
Table 6.3 Regression parameters for proposed model for subgrade soil...... 174
Table 7.1 Resilient modulus test results ...... 187
Table 7.2 traffic volume data...... 188
Table 7.3 Asphalt concrete properties...... 189
Table 7.4 Granular aggregate base properties...... 190
Table 7.5 Subgrade properties ...... 190
Table 8.1 Hydraulic Conductivity of The Materials Tested ...... 210
Table 8.2 Permeable base quality of drainage rating based on time taken to drain...... 223
Table 8.4 Drainage performance of permeable materials (ATB-90)...... 224
xv
LIST OF FIGURES
Figure Page
2.1 Sources of moisture in pavement systems (MEPDG, 2004) ...... 12
2.2 Typical permeable base pavement sections...... 19
2.3 Distribution of national calibration sections for fatigue cracking in HMA...... 49
2.4 Typical soil water characteristic curve...... 60
3.1 Typical highway cross section in ATB-90 projects ...... 68
3.2 Typical instrumentation profile for (a) bound base sections (b) unbound Base sections ...... 68
3.3 Sensors inside a hole prior to backfilling...... 69
3.4 Gradation curves for ODOT 307, ODOT 304 and No. 57 base course materials ...... 70
3.5 Material Testing System ...... 71
3.6 Pictures showing the horizontal rigid wall permeameter device...... 72
3.7 Comparison of MR for different granular base materials...... 75
4.1 Correction factor as a function of the degree of saturation...... 88
4.2 Average daily water content measurements in Base Layer (Driving Lane)...... 89
4.3 Average daily water content measurements in Base Layer (Passing Lane)...... 90
4.4 Average daily water content measurements in subbase Layer (Driving Lane).. 90
4.5 Average daily water content measurements in subbase Layer (Passing Lane) .. 91
4.6 Average daily water content measurements in top subgrade layer (top 30 cm of subgrade layer) Driving Lane...... 91
xvi
4.7 Average daily water content measurements in top subgrade layer (top 30 cm of subgrade layer) Passing Lane...... 92
4.8 Average daily water content measurements in bottom subgrade layer (at 180 cm from base surface) Driving Lane...... 92
4.9 Average daily water content measurements in bottom subgrade layer (at 180 cm from base surface) Passing Lane...... 93
4.10 COV for moisture content in base with time (Driving Lane)...... 94
4.11 COV for moisture content in base with time (Passing Lane)...... 95
4.12 COV for moisture content in subbase with time (Driving Lane) ...... 95
4.13 COV for moisture content in Subbase with time (Passing Lane)...... 96
4.14 COV for moisture content in top subgrade soil (top 30cm of subgrade layer) with time (Driving Lane)...... 96
4.15 COV for moisture content in top subgrade soil (top 30cm of subgrade layer) with time (Passing Lane)...... 97
4.16 Percent change in measured moisture content for base layer from initial water content (Driving Lane) ...... 100
4.17 Percent change in measured moisture content for base layer from initial water content (Passing Lane)...... 100
4.18 Percent change in measured moisture content of subbase from initial water content (Driving Lane) ...... 101
4.19 Percent change in measured moisture content of subbase from initial water content (Passing Lane) ...... 101
4.20 Percent change in measured moisture content for top part subgrade (top 30 cm of subgrade layer) (Driving Lane) ...... 102
4.22 Variation of moisture content in base from avg. water content...... 105
4.23 Variation of moisture content in subbase from avg. water content (Driving Lane)...... 105
4.24 Variation of Moisture Content in top part subgrade (top 30 cm of subgrade layer)...... 106
xvii
4.25 Variation of Moisture Content in bottom part subgrade (at 185 cm from top of base layer) ...... 106
4.26 Avg. moisture content with depth in cement treated base section (Driving Lane)...... 110
4.27 Avg. moisture content with depth in ODOT 307-IA base section (Driving Lane)...... 112
4.28 Avg. moisture content with depth in ODOT 307-CE base section (Driving Lane)...... 114
4.29 Avg. moisture content with depth in cement treated base section (Driving Lane)...... 116
4.30 Avg. moisture content with depth in ODOT 304 base section (Driving Lane)...... 118
4.31 Avg. moisture content with depth in Asphalt treated base section (Driving Lane)...... 120
4.32 Maximum normalized deflection measured at the center of the FWD load plate ...... 123
4.33 Computed Tensile Strains for different permeable base materials...... 127
4.34 Computed Compressive Strains for different permeable base materials...... 127
5.1 Temperature comparisons for 307-NJ section...... 143
5.2 Temperature Comparisons for 307-IA section...... 144
5.3 Temperature Comparisons for 307-CE section ...... 145
5.4 Temperature Comparisons for ODOT 306-Cement Treated section...... 146
5.5 Temperature Comparisons for ODOT-304 section ...... 147
5.6 Temperature Comparisons for ODOT 308-Asphalt Treated section...... 148
5.7 Predicted Frost Depth for 307-IA section...... 150
5.8 Frost Depth for ODOT 306-Cement Treated section...... 150
5.9 Moisture Content Profile for unbound Granular Base materials...... 152
5.10 Moisture Content profile for Bound Granular Base Materials...... 153
xviii
5.11 Moisture Content Profile for 307-NJ, using actual dry density...... 154
5.12 Effect of the Surface Shortwave Absorptivity at 0.4, 0.81 of thermal conductivity and Heat capacity, respectively...... 156
5.13 Effect of the Surface Shortwave Absorptivity at 0.7 and different thermal conductivity and Heat capacity on the prediction of temperature profile...... 156
5.14 Effect of the asphalt density on the prediction of temperature profile...... 157
6.1 Comparison between predicted and measured resilient modulus for A-4 and A-6 soils using Yang et al., (2005) model...... 165
6.2 Typical Filter Paper calibration curve ...... 169
6.3 Measured and predicted matric suction for A-4 soil ...... 171
6.4 Measured and predicted matric suction for A-6 soil ...... 171
6.5 Effect of moisture content and stress state on resilient modulus for A-4 soil.. 172
6.6 Effect of moisture content and stress state on resilient modulus for A-6 soil.. 172
6.7 Predicted versus measured resilient modulus for A-4 soils...... 173
6.8 Predicted versus measured resilient modulus for A-6 soils...... 174
6.9 Comparison between predicted and measured resilient modulus using proposed model for A-4, A-6, and A-7-6 soil (data taken form Mohammed et al, 1996, 2002, Wolfe and Butalia 2004, Khoury and Zaman 2004, Ceratti et al. 2004 and current work)...... 176
6.10 Comparison between predictions by the proposed model and AASHTO empirical equation ...... 177
7.1 Correction factor as a function of the degree of saturation...... 185
7.2 Effect of different permeable base materials...... 192
7.3 Effect of different degree of saturation ...... 193
7.4 Multi-layer linear elastic computation of vertical compressive strains versus base MR...... 194
7.5 Sensitivity to subgrade resilient modulus...... 195
7.6 Sensitivity of MEPDG to base thickness...... 196
xix
7.7 MLET calculated vertical compressive strain versus pavement depth along the total thickness of the pavement ...... 197
7.8 Sensitivity of MEPDG to AC thickness...... 198
7.9 Service life sensitivity to design criterion for different AC thickness ...... 200
7.10 Service life sensitivity to design criterion for different base resilient modulus ...... 201
7.11 Service life sensitivity to design criterion for different subgrade resilient modulus...... 201
8.1 Soil water characteristic curves for different permeable base materials ...... 210
8.2 Pavement geometry and layer configuration for ATB-90...... 212
8.3 Daily Precipitations at the site...... 214
8.4 Construction and instrumentation time line at the site ...... 217
8.5 Comparison between measured and FEM predicted volumetric moisture contents for 307-IA section ...... 218
8.6 Comparison between measured and FEM predicted volumetric moisture contents for asphalt treated section...... 219
8.7 Volumetric water content at 307-NJ section under different precipitation rates...... 220
8.8 Volumetric water content of subgrade soils at cement treated base section under different water table depth...... 221
8.9 Time to drain for 50% saturation required for different permeable base materials ...... 225
8.10 Time to drain for 50% saturation for different permeable base materials with different permeabilities...... 226
8.11 Unsaturated hydraulic conductivity for ODOT 304 permeable base materials at different gradation...... 228
8.12 Effect of hydraulic conductivity of permeable base on drainage performance...... 229
8.13 Effect of hydraulic conductivity of permeable base materials on drainage performance...... 230
xx
8.14 Effect of thickness of permeable base layer on drainage performance ...... 231
8.15 Effect of subbase hydraulic conductivity on drainage performance of permeable base materials...... 232
8.16 Drainage performance of permeable base layer with no shallow underdrain pipe...... 233
8.17 Drainage performance of permeable base layer with no deep underdrain pipe ...... 234
8.18 Effect of shoulder slope on drainage performance of permeable base materials...... 235
xxi
CHAPTER I
INTRODUCTION
1.1 Statement of the Problem
Excessive water content in the pavement base, subbase, and subgrade soils can
cause early distresses and lead to structural or functional failure of pavement, if
counteractive measures are not undertaken. As reviewed by Lytton et al (1993), water-
related damage can cause one or more of the following forms of deteriorations: a)
Reduction of subgrade and base/subbase strength, b) Differential swelling in expansive
subgrade soils, c) Stripping of asphalt in flexible pavements, d) Frost heave and reduction
of strength during frost melt, and e) Movement of fine particles into base or subbase
course materials resulting in reduction of the hydraulic conductivity.
Most water in pavements is due to rainfall infiltration into unsaturated pavement
layers, through joints, cracks, shoulder edges, and various other defects, especially in older deteriorated pavements (Mechanistic-Empirical Design Guide, 2004). Water also may seep upward from a high groundwater table due to capillary suction or vapor movements, or it may flow laterally from the pavement edges and side ditches. A study by the Minnesota Department of Transportation indicated that 40 percent of rainfall enters the pavement structure (Hagen and Cochran, 1996). 1
Providing adequate drainage to a pavement system has been considered as an
important design consideration to ensure satisfactory performance of the pavement, particularly from the perspective of life cycle cost and serviceability. The importance of providing adequate drainage in pavement has been demonstrated by numerous researchers. For example, Cedergren (1988) showed that pavement life can be extended up to three times if adequate subsurface drainage system is installed and maintained.
Forsyth et al. (1987) reported that pavements with good drainage system exhibit at least a
33 percent increase in service life for asphalt pavements and a 50 percent increase for
Portland Cement Concrete (PCC) pavements. On the other hand, Ray and Christory
(1989) observed premature pavement distresses in an undrained pavement section in
France, inferring a reduction in service life of nearly 70 percent as compared with a drained section. A recent National Cooperative Highway Research Program (NCHRP) study estimated that excess water reduces the life expectancy of pavement systems by more than half (Christopher and McGuffey, 1997).
To minimize premature pavement distresses, it is imperative to provide adequate drainage to allow infiltrated water to drain out from the base and subbase, thus avoiding saturation of base and subgrade soils. In order to remove the water from the different layers of the pavement system, the current trend is to build subsurface drainage into the system. The state-of-the art drainage design is to place a permeable base layer beneath the asphalt concrete layer (Stormont and Zhou, 2001).
The primary function of the permeable layer is to collect the infiltrating water and to move it to the edgedrains within an acceptable time frame. To effectively drain surface water infiltration, the drainage layer must be designed with an optimal combination of
2
thickness and horizontal permeability. The construction and performance of permeable
bases depend on numerous factors, such as the type of material, material gradation, layer
treatment, separation layer, pavement cross slope, shoulder material, and edge drain system.
Environmental conditions have been found to exert significant impact on the performance of flexible pavements. External factors such as precipitation, temperature, freeze-thaw cycles, and depth to water table are the main environmental factors that have exerted major influences on the pavement performance. The internal factors, such as the susceptibility of the pavement materials to moisture and freeze-thaw damage, infiltration potential of the pavement, control the extent to which the pavement will react to the applied external environmental conditions
Flexible pavement response to traffic loading largely depends on the stiffness properties of the materials composing the pavement profile. Fundamental aspects which must be considered in the design of new asphalt concrete pavements and overlays include the seasonal variations of the resilient modulus of the asphalt concrete and the corresponding variations in the resilient modulus of the subgrade. In order to incorporate the seasonal variation of moisture and temperature into the pavement design process, the seasonal changes in the moduli of various pavement layers must be determined. In other words, there is a need to quantify the effect of moisture variations on the moduli of unbound materials and the effect of temperature variations on the modulus of the asphalt concrete (AC) layer. This may be accomplished by studying the factors that affect the modulus for both layers and predicting the modulus from these factors.
3
The NCHRP project 1-37A was undertaken to develop the new M-E Pavement
Design Guide (2004). The new proposed Mechanistic- empirical Pavement Design Guide provides significant potential benefits over the 1993 AASHTO guide in achieving cost- effective pavement designs and rehabilitation strategies. Most importantly, its user- oriented computational software implements an integrated analysis approach for predicting pavement condition over time that accounts for the interaction of traffic, climate, and pavement structure. Since each state has it is own materials, different levels of construction expertise and climatic conditions, all states will be urged to implement these guidelines which rely largely upon M-E techniques for calculating response and performance using projected load, material, and environmental conditions. The eventual implementation of the mechanistic flexible pavement design procedures requires the determination of changes in material properties for an accurate evaluation of pavement life and proper determination of required layer thicknesses.
Climate is fully incorporated into the Design Guide methodology by incorporating
the Enhanced Integrated Climatic Model (EICM) software as an integral part of the
Mechanistic Empirical Design Guide procedure. The EICM is based on theoretical
models and is currently being evaluated using the LTPP database. The most important
output from the EICM for the flexible and rigid pavement design is a set of adjustment
factors for unbound material layers to account for the effects of environmental parameters
and conditions such as moisture content changes, freezing, thawing, and recovery from
thawing. Furthermore, EICM can compute in-situ temperatures at the midpoints of each
bound sublayer as well as the temperature profiles within the AC and/or PCC layer for
every hour, and the average moisture content for each sublayer in the pavement structure.
4
The FHWA microcomputer program DRIP (Drainage Requirements in
Pavements) can be used to perform the subsurface hydraulic design of highway
pavements. Among the drainage design elements, DRIP allows for the calculation of the
time to drain in the drainage layer of a pavement system.
Recent studies suggest that the performance of conventional drainage systems can only be fully understood if unsaturated flow principles are considered (Birgisson and
Roberson, 2000). Because of the complexity of the two-dimensional unsaturated flow in a
pavement section, such as modeling infiltration of precipitation and transient flow
problem, finite element method (FEM) can be used as a powerful analysis tool. The
versatility in the hydraulic conductivity modeling capability of the finite element method
makes it particularly applicable to analysis of the layering system and subdrainage
features in a pavement. FEM simulations can incorporate both unsaturated and saturated
seepage processes with material properties specific to the different layers comprising a
pavement section. Material properties and pavement layer configurations can be varied in
FEM simulations to allow for parametric studies without the need for conducting
expensive and time-consuming field experimentation.
Ohio Department of Transportation (ODOT) has adopted several types of materials specifications for use as permeable bases:(a) ODOT 307 base, including IA, NJ, and CE types, (b) ODOT 306 Cement Treated Base, (c) ODOT 308 Asphalt Treated base.
The ATB-90 research project is a field instrumentation and monitoring research that was initiated to study the effectiveness of different permeable base materials in actual service. Two sites in ATB-90 (at I-90 highway, Ashtabula County, Ohio) were selected to represent two types of subgrade soil conditions. A total of nine (9) pavement
5
sections (each section is 500 ft long) in one traffic direction were built with the studied permeable base materials: Phase I includes three monitoring sections (ODOT 304, ODOT
306, and ODOT 308), while Phase II includes six monitoring sections ( (a) ODOT 307 base with three variations designated as 307-IA, 307-NJ, and 307-CE, (b) ODOT 306-
Cement Treated Base, (c) ODOT 308-Asphalt Treated Base plus ODOT 304 as a control section). Instrumentations were installed at each section for monitoring of moisture, temperature, and frost depth. At each site, a weather station equipped with the capability to monitor the solar radiation, air temperature, wind speed, wind direction, and precipitation was installed to provide baseline ambient climatic information. Also, the monitoring study of ATB-90 sites encompasses extensive laboratory testing of the permeable base materials and subgrade soils from the sites, as well as regularly scheduled field NDE evaluations of the performance of the as-built pavement under both imposed traffic and environmental conditions.
The effectiveness of these permeable base materials in actual service has not been conclusively established in previous ODOT studies. Results predicted from this research will help in studying the effectiveness of different base materials types, and lead to base design/selection procedures which will result in extended pavement life and significant savings in the pavement infrastructure.
1.2 Objectives of the Study
The objectives of this dissertation can be enumerated as follows:
1. To present pertinent laboratory test results on these permeable base materials as
well as field monitoring data and performance test results. The effectiveness of 6
the five permeable base materials and ODOT 304 controlled fill material will be
examined based on both laboratory and field data. Conclusions of the
effectiveness of these studied materials will be presented.
2. To evaluate the effects of environmental conditions on pavement performance
over several types of materials adopted by Ohio Department of Transportation
(ODOT) to be used as permeable bases. The seasonal variation in the moisture
data for ATB-90 will be used to evaluate the impacts of different permeable bases
on the subgrade moisture variation and the effect of these variations on the
structural capacity of pavement layers. Statistical analysis techniques will be
employed to interpret the spatial and temporal variations of the measured water
contents in the base, subbase, and the subgrade. Estimation of seasonal adjustment
factor for subgrade resilient modulus will be conducted. Prediction of pavement
service life will be considered using pavement performance models.
3. To evaluate the capability of the Enhanced Integrated Climatic Model (EICM)
software to predict the seasonal variations in moisture content, temperature and
frost depth profiles for six flexible pavement sections built with six different types
of permeable base materials.
4. To present a new predictive model incorporating the matric suction and stress as
state variables to predict the resilient modulus of cohesive soils at different levels
of moisture content and stresses. The proposed model will be compared with the
experimental data and MEPDG empirical equations in predicting the influences of
moisture content on the resilient modulus.
7
5. To present the MEPDG for evaluating the effect of moisture on performance of
asphalt pavements sections built with different types of permeable base materials.
The evaluation procedures and the essential representative material properties will
be described. The impact of the most significant parameter in the design procedure
on pavement performance and service life prediction such as: thickness design
(asphalt concrete and base thicknesses), environment (moisture content variation),
and material properties (base and subgrade resilient modulus) are evaluated.
MEPDG software will be used to conduct a sensitivity analysis to study the
sensitivity of the output variables due to variations in the key input parameters
used in the design process. Analysis of stresses and strains in pavement layers
using Multi-Layer Linear Elastic Analysis (LEA) will be conducted to confirm
the MEPDG outcomes.
6. To present a calibrated FEM model for simulations of water flow in asphalt
pavements with different types of permeable base materials. The calibration
procedures and appropriate initial boundary conditions as well as essential
representative material properties will be described. Parametric study will be
performed to exam the effect of precipitation infiltration rate and ground water
table elevation on the predicted moisture contents in base, subbase and subgrade
specific to ATB-90 project. The effects of unsaturated conditions on pavement
drainage due to pavement materials characterization and pavement configuration
(geometry, layers, and drainage systems) will be evaluated. FEM analysis will be
also performed to evaluate the drainage efficiency of the bound and unbound
permeable base materials in terms of time required for reaching 50% saturation.
8
The required hydraulic conductivity of the permeable base materials will be re-
evaluated on the basis of ATB-90 pavement configurations and FEM modeling
results. Parametric study will be conducted to study the effect of different
parameters on the drainage performance of permeable base layer.
1.3 Organization of Dissertation
The organization of this dissertation is as follows:
Chapter II contains review information on evaluation of permeable base materials under asphalt pavement. Also subsurface drainage, quantification of environmental conditions, and the mechanistic empirical design approach are reviewed.
Chapter III presents pertinent laboratory test results on ATB-90 project permeable base materials as well as field monitoring data and performance test results. Conclusions of the effectiveness of these studied materials are presented.
Chapter IV presents the seasonal variation in the moisture data for ATB-90 from which estimation of seasonal adjustment factor for subgrade resilient modulus is conducted. Pavement performance under the effects of environmental conditions together with the evaluation of the Falling Weight Deflectometer (FWD) and multi-layer linear elastic analysis are used to predict pavement service life using different pavement performance models.
Chapter V presents an evaluation of the capability of the Enhanced Integrated
Climatic Model (EICM) software to predict the seasonal variations in moisture content, temperature and frost depth profiles for flexible pavement sections built with different types of permeable base materials.
9
Chapter VI presents a new predictive model incorporating the matric suction and
stress as state variables to predict the resilient modulus of cohesive soils at different
levels of moisture content and stresses.
Evaluation of the effect of moisture on performance of asphalt pavements sections
built with different types of permeable base materials using MEPDG design is presented
in chapter VII. Additionally, analysis of stresses and strains in pavement layers using
MLEA is conducted to confirm the MEPDG outcomes.
To develop an understanding of the drainage behavior of pavement layers under unsaturated conditions, Chapter VIII presents a finite element based numerical model of water flow through flexible pavements that is developed based on actual pavement geometries and material characteristics. In addition, a parametric study is conducted to
study the effect of different parameters on the drainage performance of permeable base
layer.
Chapter IX presents a summary and conclusions of this research.
Recommendations for future research are provided at the end of the chapter.
10
CHAPTER II
BACKGROUND AND LITERATURE REVIEW
2.1 Background
Excessive water content in the pavement base, subbase, and subgrade soils can
cause early distress and lead to a structural or functional failure of pavement, if counter
measures are not undertaken. Water-related damage can cause one or more of the
following forms of deteriorations: a) Reduction of subgrade and base/subbase strength, b)
Differential swelling in expansive subgrade soils, c) Stripping of asphalt in flexible
pavements, d) Frost heave and reduction of strength during frost melt, and e) Movement
of fine particles into base or subbase course materials resulting in a reduction of the
hydraulic conductivity considerably (Lytton et al., 1993).
Results from laboratory and field tests conducted on a number of roads indicated that the moduli of base and subgrade materials were strongly affected by moisture content (Yuan et al., 2003). Furthermore, a relatively rapid decrease in the level of serviceability could occur, because the pavement ability to transmit dynamic loads imposed by the traffic would be greatly weakened (Moulton, 1980 and Tangpithakkul,
1997). Movement of the wheel on a pavement with a saturated subgrade can produce a moving pressure wave, which in turn can create large hydrostatic forces within the structural section. These pulsating pore pressures significantly influence the load-carrying 11
capacity of all parts of the pavement structure (Cedergren, 1974). The freeze-thaw cycles could also cause moisture-induced pavement damage, because the moisture will migrate through the capillary fringe toward the freezing front to increase ice lenses.
The presence of water in the pavement is mainly due to infiltration through the
pavement surfaces and shoulders, melting of ice during freezing/thawing cycles, capillary
action, and seasonal changes in the water table. Sources of moisture in pavement systems
are shown in Figure 2.1. The significance of the respective routes depends on the
materials, climate, and topography.
Figure 2.1 Sources of moisture in pavement systems (MEPDG, 2004) Elsayed and Lindly (1996) noted that until the study by Ridgeway (1982), high
water table and capillary water were thought to be the primary causes of excess water in
pavements. Recently, crack and shoulder infiltration, and to some extent subgrade
capillary action, were considered to be the major routes of water entry to the pavement
(Elsayed and Lindly, 1996; Dawson and Hill, 1998). The significance of infiltration was
12
shown by an immediate increase in edge drain outflow following a precipitation event
(Ahmed et al., 1993). Van Sambeek (1989) reported that surface water infiltration can
account for as much as 90 to 95 percent of the total moisture in a pavement system. Van
Sambeek (1989) also identified transverse and longitudinal joints as major routes of
ingress. Similarly, field studies by Ahmed et al. (1997) showed that pavement-shoulder
joints were a major source of surface infiltration. For routes of egress, Dawson (1998)
noted that the lateral or median drain is the most significant route except when a highly
conductive underdrain (subgrade unsaturated hydraulic conductivity >0.1 cm/s) is
provided. Thus, infiltration through cracks and joints is thought to be the major ingress
route and engineered drainage is believed to be the major egress route.
Groundwater conditions may affect the moisture in pavement systems and may be the major factor influencing subgrade water content if the ground water table is within approximately 20 feet from the surface (Yoder et al., 1975). Capillary water and water vapor may migrate towards ground surface, thus increasing the moisture content especially in subgrades. Development of a perched water table may also increase the head buildup in subbase layers (Ahmed et al. 1993).
Ksaibati et al. (2000) reported that lowering groundwater table depth results in lowering moisture content in base and subbase layers. Water is always present in soil and granular pavement material in some forms, but free water, capillary water, bound moisture, and water vapor are the most concerns to pavement engineers.
13
2.2 General Design Considerations for Combating Moisture
A major objective in pavement design should be to keep the base, subbase,
subgrade, and other susceptible paving materials from becoming saturated or even being
exposed to constant high moisture levels over time. Four approaches commonly
employed to control or reduce moisture problems are listed below:
• Prevent moisture from entering the pavement system.
• Use materials that are insensitive to the effects of moisture.
• Incorporate design features to minimize moisture damage.
• Quickly remove moisture that enters the pavement system.
It is important to recognize that no approach can completely negate the effects of
moisture on the pavement system under heavy traffic loads over many years. Thus, it is
often necessary to employ a combination of approaches, particularly for heavy traffic
loading conditions. Summary of each of these approaches are discussed below.
2.2.1 Prevent Moisture from Entering the Pavement System
Conceptually, the best approach for reducing the detrimental effects of moisture is
to prevent moisture from entering the pavement system. An effective means for
minimizing surface infiltration is to provide adequate cross-slopes and longitudinal slopes
to drain water from the pavement surface quickly. In general, the less time the water is
allowed to stay on the pavement surface, the less moisture can infiltrate through joints and cracks. However, moisture enters the pavement system from a variety of sources, and
nothing can prevent it completely. Nevertheless, a proper design can minimize the
amount of moisture entering the pavement system. 14
2.2.2 Provide Moisture-Insensitive Materials
Another means of preventing moisture-accelerated damage is to use moisture-
insensitive or nonerodible base materials that are less affected by the detrimental effects
of moisture. However, although some materials can reduce or delay the detrimental
effects of moisture, moisture-insensitive materials by themselves may not fully address
moisture-related problems in pavements that are heavily loaded. Materials that are used
often to reduce moisture-related damage are as follow.
2.2.2.1 Cement-Treated Base
In addition to the conventional strength testing for durability, such materials
should also be checked for resistance to moisture erosion. In addition, an aggregate
subbase is recommended to prevent pumping and loss of fines from beneath the treated
base in areas with adverse site conditions (e.g., high design traffic, wet climates, and high
amounts of pumpable fines in the subgrade) (MEPDG 2004).
2.2.2.2 Asphalt-Treated Base
Hot-mix asphalt base materials can also be effective in minimizing moisture
problems in hot mix asphalt (HMA) pavements. The stripping of asphalt binder, caused
by many factors but particularly aggregate characteristics and inadequate film thicknesses, has been the major problem with asphalt-treated base (ATB). Therefore, just as with CTB, adequate film thickness of asphalt cement around the aggregates and quality aggregates are required in ATBs to ensure long-term durability. The treated asphalt layers should be constructed using high quality aggregates, and the design should
15
be consistent with that of a dense graded HMA base course layers defined in MEPDG
(2004). In general, high asphalt content ensures adequate film thicknesses around the aggregates, thereby increasing resistance to moisture.
2.2.2.3 Open Graded Base Materials
Granular materials with a high amount of crushed materials, low fines contents, and low plasticity may also be used to resist the effects of moisture. These open-graded materials provide better resistance to the effects of moisture than dense-graded materials with high fines contents. First, open-graded materials allow easier movement of moisture through the material, so the layer remains saturated for less time. Second, the reduction of fines means there is less material that can be ejected through joints and cracks. However, stability of these untreated permeable base layers is a major concern because settlement can lead to serious problems and needs to be addressed adequately.
2.2.3 Incorporate Design Features to Minimize Moisture Damage
Apart from using moisture-insensitive materials, several other design features can be used to minimize moisture damage. For conventional and deep-strength HMA pavements, the following design options can be used (MEPDG 2004):
• Full-width paving to eliminate the lane/shoulder cold joint, which is a major
source of water infiltration in the pavement structure.
• Provision of a granular layer between the subgrade and base course to reduce
erosion and to allow bottom seepage and minimize frost susceptibility that could
increase pavement roughness.
16
• Provision of adequate side ditches with flow lines beneath the pavement structure.
2.2.4 Removal of Free Moisture through Subsurface Drainage
To obtain adequate pavement drainage, the designer should consider providing three types of drainage systems: surface drainage, groundwater drainage, and subsurface drainage (also called subdrainage). Such systems, however, are only effective for “free water.” Water held by capillary forces in soils and in fine aggregates cannot be drained.
All three forms of drainage share a symbiotic relationship and should be considered together in the overall drainage design for a project (MEPDG 2004). The use of subsurface drainage has gained popularity over the past two decades, and many agencies now routinely specify permeable pavement structures to reduce moisture-related problems in pavements.
2.3 Subsurface Drainage Terminology
Subdrainage alternatives vary in complexity and cost, ranging from the provision of open-graded drainage layers tied into longitudinal edgedrains and outlet pipes to simply daylighting dense-graded bases (MEPDG, 2004). A permeable base system is the most complete subsurface drainage alternative, as it incorporates most of the drainage- related components.
Since it is often infeasible to design a drainage layer that will never become saturated; therefore, the design of the drainage layer is typically to satisfy three conditions: (a) to provide adequate permeability to transmit all infiltrated water during rain under partially or fully saturated flow conditions, (b) to limit the time that the
17
drainage layer is fully saturated to a relatively short duration of a few hours or less after
the rain stops, (c) and to provide enough structural stability to support pavement
construction and traffic load.
2.3.1 The Decision to Include Sub-Surface Drainage
There is little doubt about the detrimental impact of water on pavement systems in
a general sense. There is considerable anecdotal and experimental evidence that adequate
sub-surface drainage will increase the life expectancy of pavements (MEPDG 2004). On
the other hand, some experimental results have been uncertain regarding the benefit of sub-surface drainage. Sub-surface drainage adds to the complexity and cost of pavement construction. The decision-making methodology or criteria regarding sub-surface drainage varies with the agency. Christopher and McGuffey (1997) present results from a survey of agencies throughout the United States regarding the inclusion of sub-surface drainage elements.
These results indicate that sub-surface drainage is employed by many agencies throughout the US, although the criteria for the drainage decision are apparently not
consistent between agencies. For example, some agencies such as California always
include drainage beneath concrete pavements. Other agencies focus on the anticipated
traffic load: the heavier the traffic load, the greater the perceived need for sub-surface
drainage.
Mallela et al. (2000) present general considerations for whether sub-surface
drainage should be utilized for concrete pavements, and group these considerations into
site conditions and traffic and design conditions. Site conditions include sub-grade
18
permeability, whether the site freezes or not, and whether the pavement section is at
grade or a cut section. Conditions that indicate the need for drainage include low
subgrade permeability, freezing and wet conditions, and cut sections. Mallela et al.
(2000) also recommend that the drainage feasibility be considered with respect to past experience, anticipated paving aggregate quality, and the cost implications of including drainage.
2.3.2 Drainage components or elements
The principal elements of the state-of-the-practice sub-surface drainage system
(Christopher and McGuffey, 1997; Mallela et al., 2000) are shown in Figure 2 and
described below.
Figure 2.2 Typical permeable base pavement sections
Permeable base – In order to remove the excess moisture in the pavement, many states have adopted the use of permeable base or subbase layers. Permeable bases are used in both PCC and asphalt concrete pavement. Depending on structural requirements, the permeable base could be bound (e.g., asphalt-treated or cement-treated), or unbound. The
19
primary function of the permeable layer is to collect water infiltrating into the pavement
and to move it to the edgedrains within an acceptable time frame. The construction and performance of permeable bases depend on numerous factors, such as the type of material
(bound vs. unbound), aggregate gradation, pavement cross slope, shoulder material, and
edge drain design, among others. A properly designed and constructed permeable base
layer may function as a conventional dense graded base, supporting the pavement by distributing the loads (Apul, et. al., 2002). Therefore, to reduce the time to drain cost- effectively, MEPDG recommends a minimum laboratory permeability value of 1000 ft/day for permeable bases. Since the thickness does not have a significant effect on the time-to-drain parameter, a value of 4 inches is recommended for the thickness of permeable bases (MEPDG, 2004).
Separator layer – A separator layer is often placed between the base course and the subgrade in order to prevent mixing of the two layers. In particular, fine intrusion into the base course reduces its strength and its permeability, and consequently its ability to drain
water. Increasingly, geotextiles are used for this function.
Edgedrain – The edgedrain is usually located beneath the shoulder area. The principal
purpose of the edgedrain is to collect and remove water that drains laterally from the base course. A common edgedrain design consists of a trench filled with a coarse material
(sand or gravel), along with a perforated plastic pipe (MEPDG, 2004).
Outlet pipes - The outlet pipes serve to transport water from the edgedrain to a ditch.
These are generally non-perforated pipes that should be sized to not only carry the
expected water flow but also permit periodic maintenance and perhaps video inspection.
20
Ditches – Outlet pipes should daylight in a ditch that can carry their expected quantities
as well as surface water. The ditch should have sufficient freeboard to prevent water in
the ditch from entering the outlet pipes. Storm drains may replace the ditch in urban
settings.
2.4 Effects of Weather-Related Factors on Pavement Performance
It is well known that environmental changes are the major factor in pavement
deterioration. The effect of seasonal variation on pavement performance is generally
considered to be very important. While the modulus of the Asphalt Concrete (AC) layers
is more sensitive to the temperature variation, the modulus of unbound materials is sensitive to the variation of moisture content. These two environmental factors, temperature and moisture content, must be incorporated in the design process of flexible pavements particularly in seasonal frost areas where pavements are likely to heave during winter and then lose part of their bearing capacity during spring thaw.
2.4.1 Effect of Water Content on Resilient Modulus of Subgrade Soil
Resilient modulus, MR, of soils was introduced as an important material
parameter in the 1986 AASHTO Guide for design of pavement structures (AASHTO
1986). In the new MEPDG (2004) Design Guide (NCHRP 1-37A), the resilient modulus
also plays a major role in representing the properties of the materials in various pavement
layers. The resilient modulus of cohesive soils is not a constant stiffness property, but
highly dependent upon factors such as the state of stress, soil structures, and water
content (George 2004). Due to complexity of conducting resilient modulus testing, there 21
have been numerous efforts to develop predictive equations by incorporating state variables such as confining stress, bulk stress, deviatoric stress, and soil physical properties.
The importance of the water content in affecting the resilient modulus of soils has been well documented by the past researches. For example, Drumm, et al. (1997) showed a significant reduction of resilient modulus of A-4, A-6, and A-7 soils as the moisture content was increased above the optimum moisture content. Pezo et al. (1992),
Mohammad et al (1996), Wolfe and Butalia (2004) have observed the significant influences exerted by the water content on the measured resilient modulus of the cohesive soils. The moisture content of the subgrade soils underneath the pavement is usually varied over time. According to Uzan (1998), clayey soils underneath the pavement exhibit an increase in moisture content to about 20 to 30 percent higher than the plastic limit of the soil. This occurs during the first three to five years of pavement service.
Similarly, Elfino and Davidson (1989), Thadkamalla and George (1995), and Uzan (1998) indicated that the moisture content of the subgrade soils would vary with season until reaching an equilibrium moisture content (EMC). In view of the sensitivity of the resilient modulus of cohesive soils to the water content and stress state and the likelihood of the soils’ moisture variation underneath the pavement, it is important to develop a simple and accurate prediction equation for predicting the variation of resilient modulus due to changes in stress and moisture content of cohesive soils.
22
2.4.1.1 Resilient Modulus Models
The concept of resilient modulus has been used to represent the nonlinear stress- strain characteristics of subgrade soils. Several constitutive models for modeling resilient modulus of soils have been proposed in the past. Among them, Seed et al. (1967) proposed a relationship where resilient modulus is a function of bulk stress.
K2 M R = K1 (θ / pa ) (2-1) where
MR = resilient modulus
θ = bulk stress =(σ1+σ2+σ3 ), σ1,σ2,σ3 are three principal stresses.
Moossazadeh and Witczak (1981) proposed a relationship known as the deviatoric stress model for cohesive soils.
K2 M R = K1 (σ d / pa ) (2-2) where
σd = deviatoric stress = (σ1 - σ3).
For many slightly cohesive fine grained soils, the resilient moduli obtained from the repeated load triaxial test can be modeled as a bilinear function of the applied deviator stress. Confining pressure is considered constant in this model according to
Thompson and Robnett (1976).
M R = K1 + K2 (K3 −σ d ) σ d ≤ K3 (2-3)
M R = K4 − K5 (σ d − K3 ) σ d ≥ K3
23
where K1, K2, K3, K4 and K5 are material constants obtained from laboratory repeated tests. The disadvantage of the model is that the effect of confining pressure was not considered.
Uzan (1985) proposed a new model that considers the effect of shear stress on the resilient modulus. The Uzan Model is expressed as follows.
K2 K3 M R = K1(θ ) (σ d ) (2-4) where
θ = bulk stress
σ d = deviator stress = (σ 1 −σ 3 )
K1, K2, K3 are regression analysis constants evaluated by multiple regression analysis of the experimental data.
Uzan Model considers both the effect of bulk stress (θ) and deviator stress (σ d ),
which is directly related to the maximum shear stress τ max = σ d / 2 . Therefore Uzan model was able to resolve or remedy one of the deficiencies in the bulk stress model by considering the effect of shear stress. Witczak and Uzan (1988) modified the model by including the octahedral shear stress in the model instead of the deviator stress. The
Witczak and Uzan new model is as follows.
MR θ K 2 τoct K3 = K1( ) ( ) (2-5) pa pa pa where
2 τ = ()σ −σ oct 3 1 3
pa = atmospheric pressure 24
The stress and resilient modulus were normalized with respect to atmospheric pressure that helps in non-dimensionalizing the constants. The resilient properties of the soil depend on confining and normal stresses and deviator or shear stress states. The octahedral normal and shear stress provided a better representation for the stress states of a material in which normal and shear stresses change during loading.
The generalized model adopted by MEPDG (2004) is given below.
k2 k3 ⎛ θ ⎞ ⎛τ oct ⎞ M R = k1 pa ⎜ ⎟ ⎜ +1⎟ (2-6) ⎝ pa ⎠ ⎝ pa ⎠
The coefficients k1, k2, and k3 in the previous equations are regression constants.
Most of the State Highway Agencies in the United States do not routinely measure resilient modulus in the laboratory; however, the resilient modulus used for design was estimated either from experience or from other material properties (Georg
2004). For example, Von Quintos and Killingsworth (1998), Dai et al.(2002), Santha
(1994) and Mohammad et al. (1999) have developed prediction equations for resilient modulus by relating the regression coefficients of the models to the soil physical properties. Similarly, Carmichael and Stuart (1986), Drumm et al. (1997), Lee, et al.
(1995, 1997), Burczyk et al. (1994) and Brown and Pappin (1991) have developed prediction equations for the resilient modulus of cohesive soils based on simple laboratory tests results. A review of the above mentioned prediction or correlation models for cohesive soils indicate that none of them explicitly takes into account of the moisture effect. The current MEPDG (2004) adopts the following model to predict the change of modulus due to a change in degree of saturation of the soils:
25
M R b − a log = a + (2-7) M Ropt ⎛ − b ⎞ 1+ EXP⎜ln + km.()S − Sopt ⎟ ⎝ a ⎠ where:
M /M Resilient modulus ratio; M is the resilient modulus at a given time and M R Ropt = R Ropt
is the resilient modulus at a reference condition.
a = Minimum of log(M /M ). R Ropt
b = Maximum of log(M /M ). R Ropt
km = Regression parameter.
S = Degree of saturation
Sopt = Degree of saturation at a reference condition.
The MEPDG (2004) equation is very general and relies on empirical regression constants. More importantly, the M-E equation does not combine the effects of state of stress and water content.
2.4.2 Models for Estimating the Resilient Modulus Based on Single Soil Parameter
Many different relationships have been proposed to express the stress-dependency of the resilient modulus of soils and granular materials. One of the most widely utilized relationships for granular materials including sands and unbound aggregate base materials is the one proposed by Seed et al. (1967).
Different investigators have developed relationships between specific material properties and resilient modulus. Using a database of 250 tests on both coarse and fine
26
grained soils, Carmichael and Stuart (1985) related the resilient modulus (in ksi) to the soil class, bulk stress and water content of granular soils as follow:
Log (Mr)= 0.523 - 0.0225 w + 0.544 log θ + 0.173 SM + 0.197GR (2-8)
where w is the water content in percent; θ is bulk stress in psi; SM is a “silt factor” which is equal to one for soils classified as SM and zero for all others; and GR is a
“gravel factor” which is equal to one for soils classified as GM, GW, GC or GP and zero for all others.
Thompson and Robnett (1979) conducted an extensive testing program on 50 fine-grained surface Illinois soils to test the effect of a number of factors on the resilient modulus. They found that the break point resilient modulus, MR, in the bilinear model was significantly correlated with liquid limit, plasticity index, AASHTO classification group index, silt content, clay content, specific gravity and organic carbon content. They observed that in unconfined repeated-load triaxial compression tests, the breakpoint modulus, MR was typically about 6 psi.
Carmichael and Stuart (1985) developed the following formula for predicting the resilient modulus of both cohesive soils:
MR (ksi) = 37.431 - 0.4566 (PI) - 0.6179 (%W) - 0.1424 (S200) + 0.1791 (σ3) - 0.3248 (σd) + 36.422 (CH) + 17.097 (MH) (2-9)
where
W = water content (%)
S200 = % passing sieve #200
CH = 1 for CH soil and = 0 for other soils
and MH = 1 for MH soil and = 0 for other soils 27
2.4.2.1 Moisture Effects on Unbound Materials
The moisture sensitivity of coarse-grained materials depends on the amount and nature of its fine fraction. Clean gravels and sands classified GW, GP, SW, and SP are not likely to exhibit moisture sensitivity due to the absence of a sufficient number of the small pores necessary to create significant suction-induced effective stresses even at low moisture contents (Hicks and Monismith, 1971).
Hicks and Monismith (1971) studied the effect of degree of saturation on the k1
k2 and k2 parameters of the bulk stress model ( M R = k1θ ). Two types of aggregate were used, one well graded, subangular, partially crushed gravel, and the other a well graded crushed rock. They found that k1 decreased from the dry to partially saturated states where the comparisons are made on the basis of total stresses, while k2 remained relatively constant or decreased slightly. Monismith et al. (1967) noted that the modulus of a fully saturated material may be as much as 50 percent lower than that of the same soil in a partially saturated condition.
Studies of coarse materials containing larger amounts of fines have shown that increasing degrees of saturation above about 80 to 85 percent can have a pronounced effect on resilient modulus. Rada and Witczak (1981) concluded that changes in water content of compacted aggregates and coarse soils could cause modulus decreases of up to
30 ksi (207 MPa).
Many researchers have investigated the influence of water content on resilient modulus of fine-grained soils. Seed et al. (1962) studied the influence of “natural” water content on the resilient modulus of the undisturbed samples of the silty clay (CL)
AASHO Road test subgrade soil. Their results showed that for these soils a decrease in 28
water content of only three percent below the optimum water content resulted in a doubling of the resilient modulus value.
Rada and Witczak (1981) found that there was a critical degree of saturation at near 80-85 percent, above which granular materials became unstable and resilient modulus decreased rapidly especially when running an undrained test.
Thom and Brown (1987) found that elastic modulus tend to slightly decrease by
10-15 percent with an increase in the moisture content for a wide range of granular materials. On the other hand, an increase in the degree of saturation has an important effect on the permanent deformation behavior. Elfino and Davidson (1989) reported variations in the resilient modulus values of 7 to 41 percent for soils at different water contents.
Most fine grained soils exhibit a decrease in modulus as the water content increases, leading to increased deflections in pavement subgrade (Eric, et al., 1997).
Cohesive soils pose a problem in practice, since the pore pressure developed during traffic loading will not be dissipated immediately due to the low hydraulic conductivity of soils. As a result, the effective stresses and subsequently the strength of the cohesive subgrade soils will be decreased and may cause rutting failures in pavement when subjected to higher traffic loads.
Hardcastle (1992) reported that most if not all of the differences in resilient moduli of fine grained soils, which accompany changes in either compaction water content, or post construction changes in in-situ water contents probably occur as a result of the changes in effective confining stresses existing in the material. These changes in effective stresses take place as a result of the changes in soil suction (negative pore water
29
pressures), which usually accompany the change in soil moisture content in unsaturated soils. Therefore, when the moisture content decreases, suction along with effective stress and soil stiffness generally increase until very low moisture contents are reached.
In more recent studies by Salem et al. (2003) and Bayomy et al. (2003), regression models were developed to relate the change in subgrade modulus to the change in moisture content for various types of soils. These models were then used to predict the seasonal changes in modulus at Idaho sites using shift functions that adjust the model to the specific site conditions.
Harvey et al. (1998) tested asphalt-treated permeable base materials (ATPB) containing about 2 to 3% asphalt. The stiffness, as measured by the resilient modulus, was reduced when the material was soaked for a few days. They attributed the loss of stiffness in part to stripping of the asphalt. They found that saturated samples experienced more permanent strain than dry samples under repetitive loading. They estimated that the soaked samples reduce the predicted fatigue life of a typical California pavement by about 2 to 3 times.
Water can have other deleterious effects on base course layers. Asphalt-treated materials can suffer from stripping, where the asphalt coating debonds from the soil
(typically stone-sized particles) (Harvey et al., 1998; Hajek et al., 1992). Stripping can lead to decreased resilient modulus and strength (Harvey et al., 1998), reducing the structural contribution of the base course. Cedergren (1988) suggests that cement-treated base course materials can weaken due to loss of cement in the presence of pore pressure waves due to dynamic loading of traffic. Water is also involved in the pumping of fines
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into the base course, which reduces its permeability and compromises its structural stability ( Harvey et al., 1998).
2.4.3 Temperature Effects on Soil Resilient Modulus
Temperature has significant effects on the soil resilient modulus. The penetration of freezing temperatures into moist pavement subgrade soils can cause more severe effects than the effects of any of the water content changes likely to occur as a result of seasonal variations in precipitation. Freezing of soil moisture can transform a soft subgrade into a rigid material, at the stress levels existing in pavements. Thawing of the same material can produce a softening effect such that for some time after thawing, the material has a resilient modulus that is only a fraction of its prefreezing value
(Hardcastle, 1992).
The variation in resilient modulus of the clay before and after one cycle of freezing and thawing was recorded by Bergen and Monismith (1973). Resilient modulus values of the clay after thawing were reduced to values ranging from 52 to 60 percent of the prefreezing values. The freezing and thawing on a different CH soil (from Tennessee) exhibited resilient modulus decreases of up to 49 percent of the unfrozen value
(Thompson and Robnett, 1976).
Chamberlain et al. (1979) investigated freeze-thaw effects on resilient modulus of a low plasticity natural clay subgrade obtained by core sampling. They concluded that the decreases in resilient modulus accompanying freezing and thawing were caused by the increases in water content and decreases in unit weight that occur when soils are frozen with free access to water (open-system freezing). The recovery of the resilient modulus
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following the thaw induced decreases was attributed to decreasing water content (drying) and increasing dry density
2.5 Pavement Evaluation by Non-Destructive Measurements
Many devices are being used to perform non-destructive testing on pavements
(NDT). The most widely used NDT device in the U.S. is the FWD, which is being used to evaluate the pavement performance by monitoring the surface deflection and backcalculating the pavement layer moduli (Birgisson et al. 2000, Snyder et al. 1996,
Sargand et al. 1996, Saarenketo et al. 2002, Kestler et al. 2001, Loulizi et al. 1999, Janoo et al. 2000, Kazmierowski 1999 and Figueroa 2001). A survey done by Newcomb et al.
(1999) showed that most states are using the maximum FWD surface course deflection, subgrade modulus and backcalculated layer moduli for interpretation of deflection measurements. On the other hand, a few states use AREA method and modulus of subgrade reaction. A survey conducted by Nazef and Choubane (2001) for Florida-DOT showed that 27 U.S. states used Dynatest FWD device out of 39 surveyed states.
FWD deflection tests have been used as an integral part of the pavement structural evaluation and rehabilitation process (AASHTO 1993). The FWD measurements can be used to backcalculate the elastic moduli of various pavement layers, evaluate the load transfer efficiency across joints and cracks in concrete pavements, and determine the location and extent of voids under cracks in pavements.
The AASHTO (1993) guide permits the use of both laboratory and in-situ backcalculated subgrade moduli, but acknowledges that the moduli determined from both procedures are not equal. The guide suggests the use of a (0.33) factor to adjust the
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subgrade modulus that is backcalculated from deflection basins measured on the pavement surface layer or in the laboratory. Different ratios have been determined in other research studies on this topic. Rahim et al. (2003) documented the following ratios reported by other researchers: Ali et al. (1987) reported a ratio range of 0.18 to 2.44, and
Newcomb (1987) reported a value in the range of 0.8 to 1.3. Von Quintus et al. (1998) reported a wide range of 0.1 to 3.5, based on LTPP data base. Chen et al. (1998) reported that the laboratory values were nearly two times higher than the backcalculated values from surface deflection measurements. In summary, the lab moduli might be greater or smaller than the FWD backcalculated moduli.
Rahim et al. (2003) called for reevaluation of the 0.33 ratio suggested by the
AASHTO 1993 design guide. They compared the laboratory measured values with the backcalculated moduli from subgrade and surface course FWD deflections. The results showed that the backcalculated moduli of the prepared subgrade were in good agreement with the laboratory values. The small difference between the compared values was attributed to the difference in the tested materials volume in both tests. On the other hand, the backcalculated subgrade modulus from surface course FWD measurements increased by 40% for fine-grain soils, whereas the increase was 100% for coarse-grain soils when compared with the laboratory values. This difference was primarily attributed to confinement resulting from the overlying pavement structure.
Different programs are being used in backcalculating the modulus of elasticity (E) from FWD data. EverCalc developed by Washington DOT and Modulus developed by
Texas Transportation Institution, are among programs that are widely used. EverCalc
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program matches the measured deflections against those calculated from theory using finite difference analysis method (Newcomb and Birgisson, 1999).
Appea et al. (2000) used ELMOD backcalculation program to confirm the effectiveness of using geotextile as a separator in a pavement system built over a weak subgrade. Hildebrand (2003) used the back calculation programs MODCOMP5 and
FeBack. Noting that MODCOMP5 considers the overburden stress in its nonlinear constitutive material models, while FeBack does not. Both programs were capable of performing linear as well as nonlinear analysis, i.e.: stress dependent analysis.
Saarenketo et al. (2002) estimated the bearing capacity of the subbase materials using the deflection results from the FWD tests. Figure 2.19 shows the FWD results from spring and summer 2000 in the subbase materials of the Main Road in Rovaniemi,
Finland. They found that that the deflections measured at 0, 200, and 300 (mm) from the center of FWD load plate increased rapidly as the road surface thawed, with maximum deflection measured on April 28th. It also showed that by May 4th the deflection values had already clearly fallen. On the other hand, the deflections measured at 600, 900 and
1200 (mm) from the center of FWD loads plate, reflect the softening of the subgrade.
Birgisson et al. (2000) and Kestler et al. (2001) used the EverCalc backcalculation program to obtain the pavement layer modulus. Birgisson compared the calculated moduli with the EICM predicted moduli. The EICM accounts for changes in stiffness of the pavement asphalt layer with temperature and the stiffness of the unfrozen base with the moisture content in each layer. The backcalculated moduli for the asphalt concrete and base layers were close to the predicted EICM moduli. In the case of base layer, the
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EICM assumes that the stiffness of granular base material is insensitive to moisture content when it is unfrozen.
Kazmierowski et al. (1994) used deflection measurements at the center of the
FWD load plate to evaluate untreated, Cement and Asphalt Treated bases in Canada. The measured deflection of the Cement Treated base was 17 percent less than that of the
Asphalt Treated base. The Asphalt Treated base deflection was less by 28 percent than that of the unbound base. In his PhD dissertation, Sedtha (2000) evaluated five different base materials (standard ODOT 304, 307 NJ, 307 IA, Asphalt Treated base, and Cement
Treated base) under flexible pavement in Ohio State Route 33. The measurements showed a significant variation in all test sections. The base course sections with stabilized aggregate bases showed the least station-to-station FWD deflection variability, and the base sections with unstabilized aggregate showed the highest variability.
2.6 Calibration and Validation of the Enhanced Integrated Climatic Model (EICM)
Environmental conditions have been found to exert significant impact on the performance of flexible pavements. External factors such as precipitation, temperature, freeze-thaw cycles, and depth to water table are the main environmental factors that have exerted major influences on the pavement performance. The Internal factors, such as the susceptibility of the pavement materials to moisture and freeze-thaw damage, infiltration potential of the pavement, control the extent to which the pavement will react to the applied external environmental conditions.
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2.6.1 Importance of Climate in Mechanistic-Empirical Design
The Mechanistic Empirical Design Approach fully considers the change of temperature and moisture profiles in the pavement structure and subgrade over the design life of a pavement, through the use of a climatic modeling software referred to as the
Enhanced Integrated Climatic Model (EICM), (Design Guide, 2004).
Moisture and temperature are the two environmentally driven variables that significantly affect the pavement layer and subgrade properties as well as the load carrying capacity. Some of the effects of environment condition on the pavement base materials used in this study are enumerated below:
-Asphalt stabilized base materials have been found to possess stiffness three times higher than typically compacted unbound granular base materials. However, laboratory test results (Ashteyat, 2004) clearly indicated a strong dependency of the strength and stiffness of asphalt stabilized materials on the temperature. A considerable reduction in the strength and stiffness was observed in this material when temperature was increased.
Also, the asphalt stabilized material shows a tendency for bond loss due to moisture and freezing thawing cycles, leading to high probability of pre-mature pavement failure
(Ashtyeat, 2004).
- Cement stabilized granular base materials properties showed that both flexural strength and resilient moduli are not significantly affected by normal temperature changes.
- In general, the higher the moisture content, the lower the modulus of unbound granular base materials. However, moisture has two separate effects. First, moisture can affect the state of stresses, through suction or pore water pressure. Coarse grained and fine-grained materials can exhibit more than a fivefold increase in modulus due to the soils being 36
drying out. The moduli of cohesive soils are affected by a complex clay-water- electrolyte interaction. Second, moisture can affect the structure of the soil through destruction of the cementation between soil particles (Design Guide).
- Bound granular base materials are not directly affected by the presence of moisture.
However, excessive moisture can lead to stripping in asphalt stabilized mixtures. Cement bound granular materials may also be damaged during freeze-thaw and wet-dry cycles, as reflected in modulus reduction. It was found that a considerable reduction in the compressive strength of cement treated materials after being subjected to 15 cycles of freezing/thawing cycles. The amount of unconfined compressive strength reduction of cement stabilized granular base materials due to freezing/thawing was found to be up to
60% at 35 cycles ( Ashteyat, 2004).
In 1997, Larson et al. introduced the Enhanced Integrated Climatic Model
(EICM)version 2.0 program. The EICM 2.0 (Windows 95 version) is an upgrade to the original Integrated Model that was developed in a joint effort by the Texas Transportation
Institute, Texas A&M University and the University of Illinois in 1989. The EICM is a one-dimensional coupled heat and moisture flow program that is intended for analyzing pavement soil systems in conjunction with climatic conditions. It has the capability of generating internally realistic patterns of rainfall, solar radiation, cloud cover, wind speed, and air temperature to simulate the upper boundary conditions. EICM also has a variety of options for specifying the moisture and temperature at the lower boundary and at the interface between the subgrade and the base course. It considers the lateral and vertical drainage of the base course, which is a two-dimensional problem, in determining
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the amount of water that enters the subgrade by infiltration through the pavement surface and base course.
2.6.2 The Enhanced Integrated Climatic Model
The EICM is a one-dimensional coupled heat and moisture flow computer program that simulates changes in the behavior and characteristics of pavement and subgrade materials in conjunction with climatic conditions over several years of operation. The EICM consists of three major components:
• The Climatic-Materials-Structural Model (CMS Model) developed at the University of
Illinois.
• The Frost Heave and Thaw Settlement Model (CRREL Model) developed at the
United States Army Cold Regions Research and Engineering Laboratory (CRREL).
• The Infiltration and Drainage Model (ID Model) developed at Texas A&M
University.
The original version of the EICM, referred to simply as the Integrated Climatic
Model, was developed for the Federal Highway Administration (FHWA) at Texas A&M
University, Texas Transportation Institute in 1989. The current EICM computes and predicts the following information for the entire pavement/subgrade profile: temperature, resilient modulus adjustment factors, pore water pressure, water content, frost and thaw depths, frost heave, and drainage performance.
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2.6.3 Incorporation of EICM into the Design Guide
Climate is fully incorporated into the Design Guide methodology by incorporating the EICM software as an integral part of the Mechanistic Empirical Design Guide procedure. The user specified inputs to the EICM are entered through interfaces provided as part of the Design Guide software. The EICM processes these input and feeds the computer output to the three major components of the Design Guide’s mechanistic- empirical design framework-materials, structural responses, and performance prediction.
The most important output required from the EICM for the flexible and rigid pavement design is a set of adjustment factors for unbound material layers to account for the effects of environmental parameters and conditions such as moisture content changes, freezing, thawing, and recovery from thawing. Furthermore, EICM can compute in-situ temperatures at the midpoints of each bound sublayer as well as the temperature profiles within the AC and/or PCC layer for every hour, and the average moisture content for each sublayer in the pavement structure.
2.6.3.1 CMS Model
The CMS model is a one-dimensional, forward finite difference heat transfer model to determine frost penetration and temperature distribution in the pavement system. The model considers radiation, convection, conduction, and the effect of latent heat.
The input to the model includes the following:
• Heat capacity of the pavement materials.
• Thermal conductivity of the pavement materials.
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• Pavement surface absorptivity and emissivity.
• Air temperature.
• Wind speed.
• Incoming solar radiation.
The one-dimensional finite difference calculation performed by the CMS model uses two boundary conditions: the upper boundary at the pavement surface and the lower boundary at a constant temperature node beneath the ground.
2.6.3.2 CRREL model
The CRREL model is a one-dimensional coupled heat and moisture flow program for the subgrade soil at temperatures that could be above, below or at the freezing temperature of water. In addition, the model predicts the depth of frost. The
CRREL model uses the temperature profiles through the asphalt layers as established by the CMS model to compute changes in the soil temperature profile, and thus frost penetration and thaw settlement.
2.6.3.3 ID Model
The ID model in the EICM uses a numerical technique to compute the degree of drainage (drained area / total area) versus time of an initially saturated granular base course with lateral drainage overlaid on a permeable or impermeable subgrade. This analysis assumes that the base course is a free draining material. Pavement Evaluation
Module of the ID model evaluates the relative adequacy of the base course design in terms of the amount of time that is required to reach a critical degree of saturation.
Eventually, the more rapidly the base course can drain the more effective it will be as a 40
load carrying member of the pavement structure under wet conditions. Moreover, the infiltration module of the ID model affords the probabilistic analyses of rainfall amount patterns derived from the precipitation model or from actual rainfall amount. The output of the ID model includes the following: a) Degree of saturation of the base course, b) The degree of drainage over consecutive dry days, and c) The probability if a dry/wet base course exists.
The EICM uses a three-step process to calculate the water contents in the base and subgrade layers. First, the equilibrium water contents were determined by assuming a hydrostatic suction profile above the water table. Second, the shape of the moisture characteristic curves was established from a series of regression equations. Third, the gravimetric water contents were computed from the moisture characteristic curves for the suction estimated in first step (Lytton et al., 1993 and Birgisson et al., 2000).
Birgisson et al. (2000) provided detailed comparisons between field measurements and predictions obtained from ICM, in terms of seasonal variations in temperature, moisture content, and layer moduli, at two representative flexible pavement test sections at the Minnesota Research Project (Mn/ROAD) site. The trend of the predicted moisture content was shown to correspond well to the measured, except during spring thaw when the ICM does not account for the critical increase in volumetric moisture content.
Richter and Witczak (2001) used two versions of the ICM in evaluating the moisture prediction capabilities of the Integrated Climatic Model (ICM), Versions 2.1 and 2.6, using data collected as a part of the Long Term Pavement Performance Program
(LTPP) Seasonal Monitoring Program (SMP). It was found that the agreement between
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monitored moisture contents and those predicted using ICM Version 2.1 was poor. On the other hand, an overall agreement was observed between the monitored moisture contents and those predicted with ICM Version 2.6.
Hydenger (2003) analyzed data from SMP testing at a test site in Ohio for the seasonal variations of moisture and temperature. The analyses were performed using daily averages for air and soil temperatures and monthly measurements of soil volumetric water content. It was found that subgrade resilient modulus varies seasonally due to changes in moisture content. Subgrade resilient modulus decreases with increase in moisture content.
2.7 Evaluation of Mechanistic Empirical Design approach over permeable base materials
The 1993 AASHTO Guide for Design of Pavement Structures was based on empirical equations derived from the AASHO Road Test. That test was conducted between 1958 and 1960, with limited structural sections at one location, Ottawa, Illinois, and with modest traffic levels compared to those of the present day. As such, designs accomplished with the 1993 AASHTO guide are projected far beyond the inference space of the original data. The AASHTO Joint Task Force on Pavements (JTFP) in the mid
1990s proposed a research program to develop a pavement design guide based on mechanistic-empirical principles with numerical models calibrated with pavement- performance data from the Long-term Pavement Performance (LTPP) program. The decision was further made to use only validated state-of-the-art technologies in this development program. The research was conducted as National Cooperative Highway
Research Program (NCHRP) Project 1-37A under the oversight of an NCHRP technical
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panel with membership drawn from state DOTs representing the JTFP, the hot mix asphalt (HMA) and portland cement concrete (PCC) paving industries, academia, and
FHWA.
The main goal of the LTPP program was to improve the design practice and reflect the actual material and seasonal changes in the design process, which can be done only via a mechanistic-based design procedure. The AASHTO 1993 design guide did not allow this, and so developing a new design guide that is mechanistically based and environmentally sensitive became a natural next step to the AASHTO guide. The
NCHRP project 1-37A was launched to develop the new MEPDG (2004).
Witczak et al. (2000), reported that the guide would incorporate environmental variations through the outputs of the Enhanced Integrated Climatic Model (EICM).
Many agencies are moving toward M-E approaches for designing pavements and are quantifying climate effects on pavement material properties specific to their region.
One study performed in Washington (Lary and Mahoney 1984) examined seasonal changes in subgrade material stiffness for the purpose of predicting seasonal changes in modulus from measurable field data such as surface deflections, soil moisture content, soil suction and weather information. It was found that soil suction cells were capable of monitoring variations in subgrade moisture content. Subgrade resilient rnoduli were predicted from soil moisture content and from measured surface deflections to determine seasonal variations with the knowledge of in-situ density and moisture contents.
Similar research was done in Manitoba, Canada (Watson 1996) using environmental and pavement surface deflection data to calculate variations in the resilient moduli of a flexible pavement. Air, pavement and soil temperatures were compared to
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seasonal variations of the backcalculated pavement layer moduli and used to develop correlations between backcalculated layer moduli, temperature (for HMA layers) and thawing index (for base and subgrade layers).
Bayomy et al. (1996) developed a mechanistic-based flexible overlay design system for the state of Idaho. Six zones and their characteristics were used: 1) to determine the expected moisture changes for the various soil groups within each zone and
2) to define the duration and onset of the seasons and the corresponding subgrade conditions and resilient moduli. In areas experiencing significant subgrade frost penetration, the average year was divided into four periods: summer, Freezing transition, winter (frozen) and spring-thaw recovery. Seasonal factors were created for subgrade soils to adjust for the changes in the resilient modulus during these periods. The factors are higher for the frozen period, and lower for the thawing and we1 periods, and vary regionally within the state of Idaho.
Kannekanti and Harvey (2006) calibrated the MEPDG (2004) using data obtained from the Long-Term Pavement Performance sections scattered throughout the United
States but with very few sections from the state of California. In this work a detailed sensitivity study was undertaken. Both the cracking and faulting models show trends that agree with prevailing knowledge in pavement engineering and California experience, in some cases results were counterintuitive. On the other hand, it was also found that the models fail to capture the effect of soil type and erodibility index and that the cracking model is sensitive to surface absorption.
Galal and Chehab (2006) conducted a comparative study of the predicted performance of highway sections in Indiana using the M-E procedure with in situ
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performance; calibration efforts were conducted subsequently. The 1993 design of this pavement section was compared with the 2004 M-E design, and performance was predicted with the same design inputs. The objectives of this study were as follows (a) assess the functionality of the M-E PDG software and the feasibility of applying M-E design concepts for structural pavement design of Indiana roadways, (b) determine the sensitivity of the design parameters and the input levels most critical to the M-E PDG predicted distresses and their impact on the implementation strategy that would be recommended to INDOT, and (c) evaluate the rubblization technique that was implemented on the I-65 pavement section.
2.7.1 Inputs level
The Design Guide uses a hierarchical approach which allows the designer flexibility in selecting the design inputs based on the importance of the project and available information. Three levels of design are provided as follows:
Level 1 (highest). Level 1 input is the highest quality of data. The input data is obtained from direct testing on the actual material in question, e.g. complex modulus testing of an asphalt concrete mix. This level of input is what the M-E Pavement Design Guide uses to model the pavement structure.
Level 2 (intermediate). Level 2 input is used when direct test results are not available, but results from other testing are, and a relationship exists between them. For example, if the
Level 1 data parameter is, the resilient modulus, MR, of a soil material, but MR is not available, resilient modulus values are determined through correlations with other more standard testing procedures, such as, California Bearing Ratio (CBR), aggregate
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gradation, plasticity index and moisture content, then by using a known relationship such
0.64 as MR = 2555 CBR , the resilient modulus may be estimated. In this case, the CBR may be entered as a Level 2 input.
Level 3 (lowest). This level has the lowest level of accuracy and would typically be used for lower volume roadways. At Level 3, not only are direct test results (Level 1) unavailable, but secondary test results (e.g., CBR) (Level 2) are also not available. Level
3 permits the user to enter a value directly for the needed variable, or to enter some other basic descriptor. Default material property values derived from the LTPP database can be used.
2.7.2 Material Characterization
It is possible for a designer to mix and match the levels of input for a specific project or region. An overview of the input requirements and testing required to characterize the materials for the primary materials is provided in the following sections.
2.7.2.1 Asphalt Concrete Materials Characterization
For a Level 1 rehabilitation project, the master curve for the asphalt concrete is developed by using the Falling Weight Deflectometer (FWD) and laboratory testing on extracted cores. The FWD testing is used to measure pavement surface deflections with the asphalt modulus calculated through backcalculation (Olidis 2004). The Level 2 project eliminates the FWD testing and uses some additional resilient modulus testing while the Level 3 analysis uses a typical asphalt concrete master curve and the results of a visual distress survey to determine the field master curve.
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2.7.2.2 Chemically Stabilized Materials Characterization
Chemically stabilized materials covered in the Design Guide include lean concrete, cement stabilized, cement treated open graded drainage layers, soil cement, lime, cement and flyash treated layers. The elastic modulus of the layer is the primary input parameter for chemically stabilized materials. For each of the stabilized materials, relationships between the elastic modulus and compressive strength have been developed
(MEPDG 2004).
2.7.2.3 Unbound Granular Materials and Subgrade Materials Characterization
The Design Guide uses the AASHTO soils classification as described in
AASHTO M145 or the Unified Soil Classification (USC) definitions as described in
ASTM D 2487. Unbound materials are categorized by grain size distribution, liquid limit and plasticity index value. The designer selects the primary unbound material type using one of the classification systems and then provides further input to determine appropriate material properties to be used for design.
The primary input parameter used for design is the resilient modulus. For Level 1 designs, the resilient modulus values of unbound granular materials, subgrade, and bedrock are determined from triaxial tests (AASHTO T307). For Level 2 designs, correlation equations have been developed with more commonly used testing protocols to estimate the resilient modulus of the unbound materials. For Level 3 designs, the resilient modulus of unbound materials is selected based on the unbound material classification
(AASHTO or USCS). The Design Guide provides a general range of typical modulus
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values (based on LTPP averages) for each unbound material classification at their optimum moisture content.
2.7.3 MEPDG Performance Models
The MEPDG (2004) considers both the functional and structural performance of pavements. The structural performance of a pavement relates to its physical condition in terms of distresses and other conditions that would reduce its load carrying capabilities.
The functional performance, on the other hand, relates to how well the pavement serves the user. Pavement distresses (cracking, rutting, faulting, etc.) are a function of pavement design, construction materials, traffic, age, foundation (subgrade), and climate.
The MEPDG (2004) considers three primary distresses and other factors to predict the smoothness of flexible pavements at any given time:
• Fatigue cracking
• Thermal cracking
• Permanent deformation (rutting)
2.7.3.1 Fatigue Cracking
Fatigue cracking is the result of repeated tensile stresses induced at the bottom of the surfacing layer bending under circulating heavy loads. Fatigue cracking can take the shape of wheel path longitudinal cracks or alligator cracks. It is generally expected that most of the fatigue cracking in seasonal frost areas will occur during spring when the deflections are the highest and when the asphalt layer is still cold, causing the material to be more brittle.
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The MEPDG (2004) model for prediction of fatigue cracking is a function of both stiffness of the mix and tensile strain at the bottom of the asphalt concrete (AC) layer.
The selected model recognizes that fatigue cracking is more likely when the modulus of
AC is high, as would be the case in cold climates or for oxidized pavements.
Figure 2.3 indicates the locations of LTPP sites used in calibrating fatigue cracking in HMA pavements. Since there was no calibration site in Ohio, it is important that the
ODOT (1) validates the MEPDG output (i.e., how well does the MEPDG predict pavement distresses when compared to actual data?), and (2) obtains the necessary field data to provide a local calibration.
Figure 2.3 Distribution of national calibration sections for fatigue cracking in HMA
2.7.3.2 Thermal Cracking of AC Layer
The MEPDG (2004) thermal cracking model is based on research conducted through several NCHRP research projects. The approach uses the Enhanced Integrated 49
Climatic Model (EICM) to estimate a temperature-depth profile for the AC layer for each hour of the analysis period. The thermal analysis procedure is based on linear viscoelastic principles and requires data from the creep compliance test conducted at three temperatures (0, -10, -20 0C) or one temperature, depending upon the level of analysis and indirect tensile test conducted at -10 0C. Data from these tests are used to relate creep compliance, D(t), to relaxation modulus, Er, of the asphalt mix. This information is then used to estimate thermal stress at any given depth and time in the AC layer.
The Design Guide considers thermal strains at the surface of the AC layer and at a depth of 0.5 inches, as the top portion of the AC surface layer is more critically affected by thermal stresses.
2.7.3.3 Permanent Deformation
Permanent deformation or rutting occurs in the wheel paths in the form of longitudinal depressions and develops as the number of load repetitions accumulate.
Rutting can be classified as:
• Primary stage rutting has a high initial level of deformation primarily associated
with volumetric change, with insignificant plastic (shear) deformations
• Secondary stage rutting is also associated with volumetric changes but at a much
slower rate; shear deformations are increasing at an increased rate
• Tertiary stage rutting is associated with plastic (shear) deformation with
insignificant volumetric changes
The MEPDG (2004) procedure for estimating rutting considers primary and secondary stage rutting; tertiary rutting is not considered. The procedure models
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secondary rutting and extrapolates the secondary rutting trend to estimate primary rutting.
Rutting is estimated for the AC and granular materials for each subseason at the mid- depth of each sublayer in the pavement system. The permanent deformation for each sublayer for each subseason is added to estimate the total permanent deformation.
2.7.3.4 Estimating Pavement Smoothness (IRI)
The MEPDG (2004) procedure uses fatigue cracking, rutting, and thermal cracking to predict the smoothness of a flexible pavement structure at a given point in time. In addition, flexible pavement distresses such as potholes, longitudinal cracking, and block cracking also affect smoothness. The MEPDG (2004) procedure estimates smoothness as a function of base type, as indicated below. The designer has the option of directly considering potential of occurrence of distresses for which the ME performance models are not available.
2.8 Evaluation of Water Flow within Pavement System
There are two different types of fluid flow, saturated and unsaturated. In the saturated flow all the soil voids are filled with water, therefore the volumetric water content is equal to the soil porosity. The hydraulic conductivity is not a function of the pore suction; hence K is considered as a constant value. The driving forces that cause saturated flow are gravitational and pressure-potential gradients (Tindall and Kunkel,
1999).
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2.8.1 Current State-of-Practice
Based on saturated flow conditions, there are two different approaches to have into consideration for the hydraulic design of a permeable base. The first one is the
Steady-state flow, which assumes uniform flow conditions. The permeable base should carry the design flows that infiltrate the pavement surface. However, the difficulty to estimate the proper precipitation frequency and duration makes it not convenient. The second approach is time-to drain. Time-to drain is a parameter that allows determining the performance of a permeable base. This approach is based on flow entering the pavement until the permeable base is saturated. Excess runoff will not enter the pavement section after is saturated; this flow will simply run off on the pavement surface. After the precipitation event, the base will drain water to the drainage system.
2.8.2 Hydraulic Design of Sub-Surface Drainage
The hydraulic design approach for a subsurface drainage system is to first design the permeable base followed by the “downstream” system elements, that is, the edgedrain, the outlet pipes and ditch.
The permeable base design is most often based on the “time to drain” method.
The time-to drain approach is based on the following assumptions:
• Water infiltrates the pavement until the permeable base is saturated.
• Excess runoff will not enter the pavement section after it is saturated.
• After the rainfall event ceases, water is drained to the side ditches or storm drains through edgedrains or by daylighting.
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The main parameter of interest in the time-to-drain procedure is the time required to drain the permeable base to a pre-established moisture level. The design standard based on this parameter rates the permeable base quality of drainage from “Excellent” to
“Poor.” Table 2.1 presents guidance for selecting permeable base quality of drainage based on this method.
The MEPDG (2004) suggests a classification of “excellent” if the time-to-drain to
50% saturation is less than 2 hours. Barksdale and Hicks (1977) suggested a time of 2-6 hours for removing 50 percent of the permeable water from airport pavements. Darter and
Carpenter (1987) proposed five hours as acceptable to reach an 85 percent saturation level. Feng et al. (1999) also reported that the drainage period varied between 4 and 7 hours. On the other hand, McEnroe (1994) considered that the extent of drainage is more important than the time. McEnroe related drainage to hydraulic conductivity of materials and noted that granular materials with a hydraulic conductivity less than 0.017 cm/s do not drain at all.
Table 2.1 Permeable base quality of drainage rating based on time taken to drain 50 percent of the permeable water.
Quality of Drainage Time to Drain
Excellent 2 hours
Good 1 day
Fair 7 days
Poor 1 month
Very Poor Does not drain
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For most interstate highways and freeways, draining 50 percent of the permeable water within 2 hours is recommended (MEPDG 2004) ; however, this is only a guideline.
The objective of drainage is to remove all permeable water within a short period of time.
The FHWA microcomputer program (DRIP), as a part of the software accompanying the M-E guide, can be used to design subsurface drainage for highway pavements. Among the drainage design elements, DRIP allows for the calculation of the time to drain in the drainage layer of a pavement system. The calculations are based on two methods: Barber and Sawyer method (1952) and Casagrande and Shannon method
(1952). DRIP assumes that the drainage layer is saturated at the time to drain and that there is no additional inflow to this layer once the rainfall has ceased. Thus, the hydraulic conductivity is considered as a constant value.
The inputs to the time-to-drain design procedure include basic pavement design and material properties such as roadway geometry (cross-slope, longitudinal slope, lane width), thickness of the permeable base, porosity and effective porosity of permeable base aggregate, and permeability of the permeable base material. Using these inputs, the time-to-drain parameter is calculated for a given degree of drainage. The final design is then chosen on the basis of this information. The calculations are based on two methods:
Barber and Sawyer method (1952) and Casagrande and Shannon method (1952).
The other approach for designing permeable bases is to design for a particular infiltration rate through the pavement surface. The infiltration rate can be derived from a particular design storm (Cedergren et al., 1973), or can be based on assumed pavement crack properties (Ridgeway, 1982). The capacity of the permeable base assuming saturated flow can then be compared to the infiltration rate.
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Design of the remaining drainage system elements involves ensuring that their capacities are sufficient to handle the flow from the permeable base. For example, if longitudinal pipes are used in the edgedrain, their capacity determined by Manning’s equation must be sufficient to transport the quantity of flow produced by the base course layer. Outlets can then be sized and located to ensure that they can accommodate the flow from the edgedrains. Design details are given in other publications (e.g., Ridgeway, 1982;
Christopher and McGuffey, 1997; Malella et al., 2000 and MEPDG, 2004).
2.8.3 Design of Subsurface Drainage using Unsaturated Soil Principles
Unfortunately, the current drainage criteria used by the FHWA and AASHTO
(1998), despite having been developed on the basis of experimental field results and theoretical analyzes of infiltration, have all been performed under the assumption of saturated conditions (Casagrande & Shannon, 1952; Cedergren, 1956 and 1972; Liu et al., 1983; Carpenter, 1990; Pufahl et al., 1990). In these previous works, material permeability and gravity action were identified as the controlling factors for pavement drainability. However, most water movement near the surface occurs under unsaturated
(or partially saturated) conditions. When rain follows a dry period, the base and the subbase are usually unsaturated. The amount of water that infiltrates the base and subgrade is not only a function of permeability and the gravitational forces, but also a function of matric suction of the material (Phillip, 1969). Permeability of a porous medium varies with its degree of saturation. Hence, it is not justified to consider fully saturated condition for study of pavement infiltration. Variation of time is another factor
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that should be considered in addition to unsaturated zone. Transient flow problems are much more complex than the steady state for which classical solutions are available.
Recent studies suggest that the performance of conventional drainage systems can only be understood if unsaturated flow principles are considered (Birgisson and Roberson,
2000).
2.8.3.1 Unsaturated Flow through Pavements
The unsaturated zone is located above the water table. Within this zone, the pore spaces are usually only partially filled with water, the reminder of the voids are taken up by air. Therefore, the volumetric water content is lower than the soil porosity. Due to the fact that water in this zone is held in the soil pores under surface-tension forces, negative pressures or suction pressures are developed. In addition, in this zone both the volumetric water content and the hydraulic conductivity are function of this suction pressure. The soil volumetric water content is held between the soil grains under surface-tension forces that are reflected in the radius of curvature of each meniscus. The higher the volumetric water content, the larger the radii of curvature and the lower the tension heads. And due to the change in volumetric water content, the hydraulic conductivity is not constant. The hydraulic conductivity content increases with increasing the volumetric water content
(Freeze and Cherry, 1979).
Many of the differences between saturated and unsaturated flow are because hydraulic conductivity during unsaturated flow is a function of the amount of the water content (or suction or saturation), and is not constant as is assumed for saturated flow.
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Another important difference is that unsaturated flow is driven in part by suction gradients, which can result in upward or lateral flow in some situations.
Conventional drainage systems are only designed to intercept saturated flow driven downward by gravity. A further complication is that unsaturated flow properties and behavior are often hysteretic, that is, they depend on whether the material is experiencing wetting or drying conditions. Hysteresis affects how conductive a soil or pavement layer is as well as how much water (or degree of saturation) it will retain under unsaturated conditions.
Soil characteristics play an important role in the infiltration rate. Total infiltration of any layer depends upon its porosity, thickness and quantity of water or other liquid present. Soil texture, structure, organic matter, root activity, and other physical properties determine the magnitude of the porosity of a given soil (Tindall and James,
1999).
2.8.3.2 Soil Suction
Soil suction is comprised of two components: matric suction and osmotic suction.
The matric suction represents attraction due to capillary and surface adsorptive forces of unsaturated soils; the osmotic suction represents attraction of water due to the presence of dissolved salts in the pore fluid. Soil suction is a negative pressure, opposite to that of atmospheric pressure. In soil mechanics convention, soil suction is expressed as a positive value.
ψ= (ua - uw)+π (2-10) where ψ is the total soil suction, (ua - uw) is matric suction and π is the osmotic suction.
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Devices and methods for measuring soil suction and soil-water characteristics curves are numerous. The filter paper method, specified in ASTM D 5928-99, is a relatively simple and inexpensive test method that covers the full range of suction.
According to Fredlund and Rahardjo (1993), the filter paper method is the only method that is capable of measuring the total suction and the matric suction simultaneously. The filter paper method is based on an assumption that a filter paper will reach equilibrium with respect to moisture flow within a soil. If a filter paper is placed in contact with the soil, the matric suction is measured. If the filter paper and soil are not in contact (but still in the same airtight container), the total suction is measured. After achieving equilibrium, the water contents of the soil and filter paper are measured and the suction in the soil is calculated using a relation between filter paper water content and suction.
2.8.3.3 Soil Water Characteristic Curve (SWCC)
The SWCC is defined as the variation of water storage capacity within the macro- and micro-pores of a soil, with respect to suction (Fredlund and Xing 1995). This relationship is generally plotted as the water content (gravimetric, volumetric, or degree of saturation) versus the soil suction. Several studies have been conducted to develop empirical equations for representing the SWCC (Zapata 1999). In general, the equation proposed by Fredlund and Xing (1994) has been found to agree with an extended database and was adopted in the MEPDG (2004). A significant amount of effort was expended to obtain the fitting parameters of the Fredlund and Xing equation based on the soil index properties (Zapata 1999).
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The shape of the soil-water characteristic curve is highly dependent on the material type. There is considerable variation in the SWCC due to different measurement methods, different soil sampling techniques, and inherent variability between samples
(Vanapalli et al. 1999). Fine-grained soils (such as clays) generally have higher matric suction than coarse-grained soils, while loose clays can undergo large volumetric changes as a result of changes in suction (Heath et al. 2004).
Water flow in the unsaturated zone primarily is due to volumetric water content, soil suction, and gravitational potential (Tindall and Kunkel, 1999). Due to the presence of air within part of the pore channels, water movement is obstructed and it only flows through the finer pores or in films around the soil particles. Hence, the drainage characteristics of unsaturated soil depend on the volumetric moisture content in soils and the fact that volumetric moisture content is less than the saturated value leads to: 1) development of suction within the soil mass, and 2) reduction of hydraulic conductivity.
Hence, two material properties are needed to describe the drainage behavior of soils at any given saturation level, namely the suction present in the soil at a given saturation level, and the corresponding hydraulic conductivity.
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Figure 2.4 Typical soil water characteristic curve
Figure 2.4 (Fredlund and Xing, 1994) shows a typical soil water characteristic curve, displaying the relationship between volumetric water content and soil suction.
The air-entry value of the soil is the matric suction at which air starts entering the largest pores in the soil. Residual water content is the water content of the soil when a large amount of suction pressure is required to remove the additional water from the initially saturated soil. The desorption curve differs from the absorption curve due to hysteresis.
There are primarily two ways of obtaining hydraulic conductivity curves for soil either by direct measurement or by estimation. Direct measurement of hydraulic conductivity curves is tedious and time consuming. Therefore, hydraulic conductivity curves tend to be estimated from soil-water characteristic curves, analytical models, and grain size curves. The hydraulic conductivity models developed over the years vary in complexity from purely empirical methods to more sophisticated closed-form solutions.
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2.8.3.4 Hydraulic Conductivity Curve Models
In 1957, Gardner developed one of the first interpolation functions for the hydraulic conductivity curve, namely:
k k(ψ) = s (2-11) 1+ A .ψβ ()k
where
kS is the saturated hydraulic conductivity.
ψ is the pore suction.
Ak, β are empirical curve fitting coefficients.
Some other well known hydraulic conductivity curves model includes Brooks and
Corey (1966), Green and Corey (1971), and Van Genuchten (1980). According to Ariza
(2002), the comparison of these four models indicated that Gardner’s model is merely empirical and it is sensitive to its coefficients. The Brooks and Corey model does not perform well at low suction values and relies on a difficult to obtain l value. This difficulty also applies to Van Genuchten’s model. Green and Corey’s model is simplest to calculate with the least amount of experience.
2.8.3.5 Finite Element Analysis of Pavement Drainage
A FE program for simulating pavement drainage should be capable of handling transient, two-dimensional, saturated/unsaturated flow. In addition, the FE program provide relatively sophisticated models for near-surface processes. Climatic data should be easily input into the FE program. The ability to incorporate a number of different functions for describing the moisture characteristic curve (moisture content vs. suction)
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and unsaturated hydraulic conductivity functions (hydraulic conductivity vs. suction) is also important.
In this research, the SEEP/W program (GEO-SLOPE, 2004) is used to simulate pavement drainage. SEEP/W is a 2-D finite element software that can be used to model moisture movement and pore-water pressure distribution within porous materials such as soil and rock. It can model both saturated and unsaturated flow. SEEP/W includes three executable programs: DEFINE, for defining the model, SOLVE for solving the problem, and CONTOUR for presenting the results in a graphical form.
SEEP/W assumes that flow in unsaturated soil above the water table follows
Darcy's law in a similar manner to flow in saturated soil. The flow is proportional to the hydraulic gradient and the hydraulic conductivity. The major difference between saturated and unsaturated flow in SEEP/W is that, in a saturated soil, the hydraulic conductivity is insensitive to the pore-water pressure; whereas, in an unsaturated soil, the hydraulic conductivity varies greatly with changes in pore-water pressure.
The governing equation is Richards' equation.
∂ ⎛ ∂H ⎞ ∂ ⎛ ∂H ⎞ ∂Θ ⎜kx ⎟ + ⎜k y ⎟ + Q = (2-12) ∂x ⎝ ∂x ⎠ ∂y ⎝ ∂y ⎠ ∂t where
H = total head, kx = hydraulic conductivity in the x direction
ky = hydraulic conductivity in the y direction
Q = the applied boundary flux,
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Θ = volumetric water content, and t = time.
This fundamental partial differential equation states that the difference between the flow entering and leaving an elemental volume at a point in time is equal to the change in volumetric water content. As it can be seen, Richards’ equation can be used for saturated and unsaturated conditions. The right part of the equation would become zero in steady-state conditions.
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CHAPTER III
ATB-90 RESEARCH PROJECT
Providing free-draining materials as a base layer under the asphalt pavement has been adopted by many state highway agencies to prevent premature water-related pavement failure. The permeable base materials should possess not only high permeability but also high strength and stiffness to sustain traffic loads. In Ohio, there are four unbounded aggregate base materials specifications and two types of bounded base materials. This chapter presents pertinent laboratory test results on these permeable base materials as well as field monitoring data and performance test results. The effectiveness of the five permeable base materials and ODOT 304 controlled fill material is examined based on both laboratory and field data. Conclusions on the effectiveness of these materials are presented in this chapter.
3.1 Overview of Work Performed
The ATB-90 research project is a field instrumentation and monitoring research to study the effectiveness of different permeable base materials in actual service (Liang,
2007). The main tasks of this research project can be broadly categorized into laboratory study and in-situ monitoring. The laboratory study used various testing devices to determine the mechanical properties and hydraulic conductivity of ODOT specific permeable base materials and ODOT 304 fill material. The mechanical properties 64
investigated included resilient modulus, permanent deformation, Mohr-Coulomb strength parameters, and durability to resist freeze and thaw cycles. The effects of material types, gradations, degree of saturation, bonding agents used (asphalt treated and cement treated), temperature, freeze-thaw cycle, confining pressure on the measured mechanical properties and hydraulic conductivity were fully investigated. Mathematical models were developed to successfully predict the behavior of resilient modulus and permanent deformations, which can be used for future applications in the mechanistic-empirical pavement design.
In-situ monitoring of environmental parameters, such as moisture content, temperature profile, and frost penetration depth were carried out at two pavement construction projects on I-90 in Ashtabula County. The first site with predominantly granular subgrade soils (A-1 soils) was instrumented for three pavement sections built with cement treated, asphalt treated, and controlled 304 base materials. The second site with predominantly cohesive subgrade soils (A-6 soils) was instrumented for six pavement sections built with cement treated, asphalt-treated, 307-NJ, 307-IA, 307-CE, and ODOT 304 base materials. The recorded data provided not only spatial but also temporal variations of water content and temperature in the base, subbase, and subgrade underneath the pavement. In particular, the measured moisture variations underneath different permeable base materials provided quantitative data to assess if the subgrade became fully saturated as reported in other ODOT studies. As a part of the field study, both FWD data and International Roughness Index were obtained for evaluating structural and service performance of the as-built pavement under in-service conditions.
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3.2 Research Program
A total of nine asphalt pavement sections, each 500 ft long, were instrumented in a reconstruction project at I-90 in Ashtabula County, Ohio. Two separate sites, each with distinctive subgrade soil conditions (one is sandy subgrade soil, while the other one is more of cohesive subgrade), were chosen for this study. Site I (ATB-90-19.56) was mainly consisted of sandy subgrade soils, where three different types of base materials were used to build up the monitoring pavement sections: ODOT 304, ODOT 306-Cement
Treated base, and ODOT 308-Asphalt Treated base. Site II (ATB-90-0.00) was mainly consisted of cohesive subgrade soils, where all five ODOT specification permeable base materials and ODOT 304 were used to build up the monitoring pavement sections. TDR readings at Site I showed that most of the subgrade sands were saturated and remained saturated due to a bathtub effect; that is, during construction of base course, the rain infiltrated into the sandy subgrade directly underneath the pavement and remained there due to the existence of low permeability cohesive soils underneath the shoulders. Since this is somewhat an abnormal behavior, site I monitoring data will not be discussed in this study.
3.2.1 Instrumentation Layout
The instrumentation embedded at Site II includes TDR (time domain reflectometer), temperature probes, and frost depth probes. The Campbell Scientific TDR
( TDR100) was used. Temperature probes (TP 101 thermistors, a temperature sensitive resistor) manufactured by Measurements Research Corporation (MRC) were used. The
MRC probe consists of individual, but interconnected probes (an acrylic pipe that 66
contained 15 thermistors for measuring the subbase and subgrade temperature), together with a specially fabricated stainless steel metal rod containing three thermistors for temperature measurement of asphalt concrete layer.
The frost depth probes were developed by the U.S. Army Corps of Engineers
Cold Regions Research and Engineering Laboratory (CRREL). The principle of the probe is based on the concept that the electrical resistivity of ice is much greater than that of unfrozen water. Therefore, the points at which the greatest changes in electrical resistivity, or minimal changes in voltage, occur indicate the approximate depth of the frost boundary.
In order to monitor climate conditions at the site, a weather station was installed to record on an hourly basis solar radiation, air temperature, wind speed, wind direction, and precipitation (both rainfall and snow fall). The cross section of the pavement section is shown in Figure 3.1 These sections were actually fill sections. The depth and spacing of each sensor are depicted in Figure 3.2 for the bound and unbound base material sections. For each monitored section, two complete duplicate sets of sensors were installed, one beneath the centerline of the driving lane and the other one beneath the centerline of the passing lane. Figure 3.3 shows a picture of sensors installed in a 16 inch hole prior to backfill.
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Figure 3.1 Typical highway cross section in ATB-90 projects
Figure 3.2 Typical instrumentation profile for (a) bound base sections (b) unbound Base sections
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Figure 3.3 Sensors inside a hole prior to backfilling
3.2.2 Material Specifications
ODOT materials specifications provide five different types of permeable base materials. ODOT 307 is further classified into CE, NJ, and IA. The mean gradation curves of these three unbound granular base materials are shown in Figure 3.4 Two bound granular base materials were specified in ODOT materials specifications. ODOT
306 is a cement treated base material, which is made by compacting AASHTO #57 stones with cement at 250 lb per cubic yard and a water cement ratio (w/c) of 0.36.
ODOT 308 is asphalt treated base, which is a mixture of AASHTO #57 stone with 1.5 to
3.5 percent by weight of PG64-22 asphalt binder. The gradation of AASHTO #57 stone
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and ODOT 304 (typical base material) are also shown in Figure 3.4 for comparison purpose.
The HMA layer in both projects was constructed in two lifts of ODOT 302 and two lifts of fine surface course layers. The total thickness of the HMA was 12.75 inches at site I and 15.25 inches at site II.
% Passing 100 90 80
70 307 NJ 60 307 IA 50
40 307 CE 30 304 20
10 NO. 57 0 No.200 No.16 No.50 No.30 No.4 3/8" 3/4" 11/2 2" " Sieve Size
Figure 3.4 Gradation curves for ODOT 307, ODOT 304 and No. 57 base course materials
3.2.3 Structural Stability of Permeable Base Materials.
Resilient modulus test was performed using a repeated load triaxial apparatus for both bounded and unbounded materials under drained condition according to AASHTO
T294-94 procedure. ODOT 304 base material was tested at fine, median and coarse gradations and ODOT 307 (NJ, IA & CE) were tested at median gradation. Freeze-thaw durability of cement treated base material was also tested at 0, 5, 15, 25, 35 freezing and
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thawing cycles. Samples of asphalt treated base materials were tested at 32°F, 77°F and
104°F.
Permanent deformation test was conducted using a repeated load triaxial apparatus. ODOT 304 and ODOT 307 were tested at median gradations and asphalt treated samples were tested at 77°F and 104°F temperatures, respectively. The test procedure for studying permanent deformation behavior for various stress levels consists of subjecting the specimen to stress paths with a constant stress ratio q/p, where q is the deviator stress and p is the mean stress. Several stages of increasing amplitudes of cyclic stress application would be carried out. The increase in cyclic stress, however, was accompanied with an increase in confining stress to maintain a constant q/p ratio. Each stress level includes a total of 40,000 cycles of load repetition. A Material Testing System
(MTS) was used to apply the cyclic loading as shown in Figure 3.5.
Figure 3.5 Material Testing System
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Permeability tests were conducted using a rigid wall permeameter as shown in
Figure 3.6. The samples of No. 57, ODOT 304 and 307 materials were compacted directly in the permeameter. Cement treated and asphalt treated samples were prepared in a wooden mold and then transferred into the permeameter.
Figure 3.6 Pictures showing the horizontal rigid wall permeameter device.
3.3 Project Summary and Observations
The main tasks of this research project can be broadly categorized into laboratory study and in-situ monitoring. The laboratory study used various testing devices to determine the mechanical properties and hydraulic conductivity of ODOT specific drainable base materials and ODOT 304 fill material. In-situ monitoring of environmental parameters, such as moisture content, temperature profile, and frost penetration depth were carried.
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3.3.1 Summary of Laboratory Work
The results obtained from the permeability tests are shown in the Table 3.1. One can observe that permeability of the treated (bounded) permeable base materials is higher than the unbound materials, except for No.57 stones. ODOT 304 fine gradation showed much lower permeability compared to 304 median and coarse gradations. ODOT 307 NJ,
IA and CE median gradations materials showed similar permeability, with 307 IA being less permeable compared to others. Cedergren (1974) recommended that permeability of a drainage base layer on any pavement system should be around 1 cm/sec (3000 ft/day).
It appears that ODOT 304 coarse gradation, ODOT 307 NJ, CE median, NO.57 aggregate and the bounded materials satisfy the required permeability.
The resilient modulus test was conducted by following the AASHTO test protocol. A bar chart is presented in Figure 3.7 for comparison of resilient modulus for all specimens at different stress and test conditions. The Resilient modulus for ODOT
304 dense graded materials is sensitive to percentage of fine contents. ODOT 304 coarse,
ODOT 307 (NJ, IA & CE) median gradations and No.57 aggregate all showed similar resilient values. Both cement treated and asphalt treated samples possess three times greater MR values than the unbounded base materials. For cement treated samples, a decrease in resilient value is observed with an increase in freezing and thawing cycles.
For asphalt treated samples, a decrease in resilient modulus was observed with an increase in temperature.
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Table 3.1 Comparison of Permeability of different permeable base materials Permeability Results
Material k (cm/s) k (ft/day)
304 – Fine 0.073 206
304 – Median 0.5 1417
304 – Coarse 1.92 5443
No. 57 9.37 26563
307 – NJ Fine 0.788 2234
307 – NJ Median 1.349 3824
307 – IA Fine 0.308 873
307 – IA Median 0.803 2277
307 – CE Fine 0.937 2654
307 – CE Median 1.307 3703
Cement Stabilized 8.94 25345
Asphalt Stabilized 8.84 25061
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3000
2500
2000
1500
1000 Resilient Modulus (MPa) Modulus Resilient
500
0
se E 7 yc yc yc -NJ -IA 3F 7 o.5 C Cyc C 3 77F 104F oar N 5 Cyc - - C 307 30 307-C - 0 C 35 B 304 Med Fine-sat B - T 4- TB- B- 25 ATB A 304 CT C T ATB- 30 CTB- 15C CTB Material Type
Figure 3.7 Comparison of MR for different granular base materials Permanent strains accumulated at each stress ratio after 40,000 load cycles for different materials are summarized in Table 3.2. One can see that accumulation of strains in asphalt stabilized aggregate materials is sensitive to test temperature. Unbounded base materials have exhibited relatively smaller permanent strain when compared with ODOT
308 asphalt treated base materials tested. With increase in stress ratio, a considerable increase in permanent strain is observed for all materials tested.
From Table 3.2, one can see that the asphalt stabilized material exhibits the highest tendency for rutting, even though it possesses higher resilient modulus than
ODOT 304 and ODOT307. Furthermore, it appears that ODOT 308 asphalt treated base material may exceed the limit criteria for rutting as stipulated in Makiuchi and Shaekel
(1976).
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Table 3.2 Accumulation of permanent strains of different materials at different stress ratio after 40,000 load cycles Material Permanent Strain @ 40,000 Cycles Type 308- 308- ODOT ODOT ODOT ODOT Asphalt Asphalt 304 307 NJ 307 IA 307 CE Stress Treated- Treated- Fine Median Median Median Ratio 770F 1040F 0.5 0.0077 0.0077 0.002778 0.002571 0.004759 0.003828 1 0.028225 0.041187 0.008325 0.004621 0.008484 0.009524 1.5 0.089401 0.0692 0.020494 0.022426 0.019686 0.013204
3.3.2 Summary of Field Monitoring Work
Field work included planning, calibrating, installation, and monitoring of various types of sensors at two instrumentation sites. The sensors used in the project included the
TDR for moisture, thermistors for temperature, and electric resistivity probe for frost depth. Monitoring and recording of sensor data was very successful from the beginning of linking up the data acquisition system until now.
Subsurface investigation and detailed soil classification of existing subgrade soils were conducted to determine soil profiles and fundamental soil properties. Ground water table was monitored using piezometers. The soil boring log and piezometer readings confirmed that there was no ground water near the ground surface.
Installation of instruments was completed by August 2002 and October 2003 at
Site I and Site II, respectively. However, TDR measurements at Site I contain many signals that can not be used to determine the inflection point of the wave form, thus rending it very difficult to estimate the dielectric constant. On the other hand, TDR measurements at Site II were of good quality where clear inflection point was measured and used for interpretation of in-situ water content.
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3.3.3 Non-Destructive Testing
The Non-Destructive FWD testing was carried out on the pavement sections during and after construction at both sites. The measurements were taken on the center line and outer wheel path of the driving lane. The load-response classification of the as- built pavement layers was performed using the deflection-load relations, taken from the center of FWD loading plate. The classification analysis showed that the newly constructed pavement sections fall in the linear elastic category. The uniformity of each test section was determined using the COV of the normalized deflections measured on the surface course layer at the center of FWD loading plate. All the newly constructed pavement sections showed uniform load responses. The FWD measurements on the surface of subgrade, unbound base and subbase materials nevertheless showed some variability in the measured deflections, which may be attributed to the spatial variability of compacted pavement layers.
The spreadability analysis (SPR) was used to quantify the FWD basin curvatures.
Analysis of SPR indicated an existence of a stiff layer underneath Phase II site subgrade.
Backcalculations of subgrade layers moduli were carried out using the FWD deflection measurements on both subgrade and surface course layers. Boussinesq’s solution for a linear-elastic, semi-infinite half space material was used to develop the backcalculation equations for subgrade soils. The backcalculation of subgrade moduli was based on the deflection measurements at both the center of the loading plate and 12 inches from the center of the plate on the subgrade. Additional backcalculation of subgrade moduli was performed using the surface layer FWD tests data measured at 36
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and 60 inches, respectively, from the center of the loading plate. A comparison between the backcalculated moduli of subgrade soils using both measurements was presented and compared with the AASHTO 1993 typical values.
EverCalc program was used for backcalculations of moduli of pavement layer. All backcalculations were accomplished within acceptable RMS errors. The existence of a stiff subgrade layer at Phase II site was accounted for in running the backcalculations. A comparison between the backcalculated and laboratory determined moduli was presented for different base course materials and for subgrade (A-4a) soils at phase I site. The comparison showed a significant discrepancy between the backcalculated and laboratory measured moduli. Nevertheless, the relative trend of the resilient modulus between all test materials was the same, indicating that the Cement Treated materials were the stiffest, followed by the Asphalt Treated materials, then the ODOT 307 materials, and finally the ODOT 304 materials.
The ProVAL software was used to calculate the International Roughness Index
(IRI) and Ride Number (RN) based on the profile test results. The profiling test has been performed two times at Phase I site and only once at Phase II site. The first test was done on January 7, 2003 on the intermediate surface course at Phase I site. The second test was done on March 10, 2004 on the final surface course layer at Phase I site and on the intermediate surface course layer at Phase II site. The International Roughness Index
(IRI) and Ride Number (RN), after one year of pavement construction, at Phase I site, showed no significant difference between the Cement Treated, Asphalt Treated, and
ODOT 304 sections. All sections, at Phase I site, were rated as Good conditions according to the ODOT criteria.
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3.4 Summary and Conclusions
Based on moisture data in the six pavement sections at Site II, the relative comparison of drainage effectiveness was analyzed. Although there was some difference in the measured moisture data among these six sections, these differences cannot be used exclusively to warrant a conclusion that ODOT 304 may not work as permeable base materials. Detailed analysis of the comparisons is provided in the following sections. The ultimate judgment regarding these permeable base materials and ODOT 304 material should be based on observations of the long-term pavement performance. This should include observations of pavement distress, such as cracking, and rutting. The studied pavement sections are relatively new; therefore, continuing the pavement NDE effort, including FWD and profile measurement, is highly recommended.
Laboratory permeability tests and resilient modulus tests of ODOT 304 with 15% fines (labeled in the report as ODOT 304 fine grading), however, showed less than adequate permeability value and high sensitivity of resilient modulus to saturation.
Therefore, it is concluded that the amount of fines allowed in the ODOT 304 should be re-evaluated by ODOT engineers.
3.4.1 Saturation in Subgrade
There was no evidence of full saturation in the subgrade soils at Site II, except for the ODOT 304 section where the soil remained saturated briefly from May 12, 2004 until
October 16, 2004 for 157 days. The TDR reading at Site I was mostly unreliable due to signal attenuation problems caused possibly by the infiltration of salts in the soils resulting in an increase in bulk EC. Since Site I is primarily situated on the cohesionless 79
subgrade soils, saturation of the sandy soils is not as critical as saturation of cohesive subgrade soils at Site II. Nevertheless, forensic investigation of Site I would be desirable to evaluate the exact cause of the TDR reading issues and to obtain physical evidence if the sandy soils were indeed saturated.
3.4.2 Drainage Efficiency
Based on laboratory permeability test results of various ODOT specific permeable base materials, the following order of drainage efficiency can be established into three categories: (a) ODOT 306 Cement Treated base (25345 ft/day) and ODOT 308 Asphalt
Treated base (25061 ft/day), (b) ODOT 307 NJ median gradation (3824 ft/day), ODOT
307 CE median gradation (3703 ft/day), and ODOT 307 IA median gradation (2277 ft/day), and (c) ODOT 304 medium gradation (1417 ft/day). However, It should be pointed out the permeability of unbound base materials are very sensitive to gradation variations, particularly the percent of fines in the material. ODOT 304 gradation band permits the presence of fines, which could significantly lower the permeability of the materials. Laboratory permeability test results indicated that ODOT 304 fine may not meet the 1000 ft/day permeability requirement proposed by the Mechanistic- Empirical
Design Guide (MEPDG).
3.4.3 Structural Stability Characteristics
The effectiveness of the five permeable base materials and ODOT 304 controlled fill material was studied based on extensive use of the mechanical properties of materials obtained in the laboratory.
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3.4.3.1 Resilient Modulus
The laboratory determined resilient modulus for all ODOT specific permeable base materials showed that cement treated base materials exhibit the highest modulus values, even after 15 cycles of freeze/thaw conditioning. The asphalt-treated base materials exhibit relatively higher resilient modulus values than the unbound base materials. However, the resilient modulus of the asphalt treated base material decreases with an increase in temperature. At 104 °F, the resilient modulus of the asphalt treated base materials possesses the values of resilient modulus similar to those of the unbound base materials. The resilient modulus of asphalt treated base materials shows reduction due to soaking of the specimens in water. The resilient modulus of the unbound base materials, particularly ODOT 304 fine gradation, is sensitive to the fine content and saturation.
3.4.3.2 Permanent Deformation
The resistance to permanent deformation of various ODOT specific permeable base materials can be ranked from high resistance to low resistance as follows: Cement- treated base, ODOT 307 CE, ODOT 307 IA, ODOT 304, ODOT 307 NJ, ODOT 308
(asphalt treated base) at 77 °F, and ODOT 308 at 104 °F. The asphalt treated base materials are highly susceptible to rutting if the temperature is high. It is also noted that the higher the stress ratio, defined as the deviatoric stress divided by deviatoric strength, the more accumulation of the permanent deformation for a given material at a given stress condition.
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3.4.3.3 Durability
The durability of cement treated base materials refers to the loss of strength and stiffness as a result of freeze and thaw cycles. Laboratory test results of the ODOT cement treated base materials showed that the material possessed excellent resistance to freeze and thaw effects. The durability of the asphalt treated base materials (ODOT 308) refers to the ability to maintain strength and stiffness after the samples have been conditioned under the water stripping test procedure. All the asphalt treated samples disintegrated following the conditioning cycles. From the durability point of view, the cement treated base materials are superior to the asphalt treated base materials.
3.4.4 Long-Term Performance Evaluation
As a part of field study, both FWD data and International Roughness Index were obtained for evaluating structural and service performance of the as-built pavement under in-service conditions.
3.4.4.1 FWD Test
The FWD tests conducted on the pavement surface over the life span of the pavement can help determine the structural load carrying capability of the pavement layers over the service life. By comparing the FWD test results at different times of the year (from spring to fall to winter), one can also ascertain the seasonal influences on the pavement load carrying characteristics. By correlating the measured in-situ water content in subgrade, subbase, and base with the FWD test data, the influence of water content on the structural performance of the pavement can be investigated. Because of the short
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duration of the pavement life, the history of FWD test data is insufficient for making any meaningful observation yet.
3.4.4.2 International Roughness Index (IRI)
The International Roughness Index of the instrumented pavement sections were determined from the profile measurements. Since the pavement is relatively new, there was no discernable difference between the different pavement sections built with different permeable base materials.
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CHAPTER IV
EFFECTS OF USING PERMEABLE BASE LAYER ON THE SUBGRADE MOISTURE
REGIME AND THE OVERALL PAVEMENT PERFORMANCE
This chapter presents evaluation of the effects of using open-graded base layer on the subgrade moisture regime and the overall pavement performance. The evaluation is based on an extensive use of the mechanical properties of materials obtained in the laboratory, seasonal measured environmental data, and backcalculated pavement layer data. Results obtained is used to assess the impacts of the presence of the permeable base layer on the variation of moisture in the subgrade and on the overall structural capacity of the pavement structure. The seasonal variation in the moisture data for ATB-90 is used to evaluate the impacts of different permeable bases on the subgrade moisture variation and the effect of these variations on the structural capacity of pavement layers. Statistical analysis technique is employed to interpret the spatial and temporal variations of the measured water contents in the base, subbase, and the subgrade. Estimation of seasonal adjustment factor for subgrade resilient modulus is conducted. Predictions of pavement service life are conducted considered using different pavement performance models.
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4.1 Introduction
Flexible pavement response to traffic loading largely depends on the stiffness properties of the materials composing the pavement profile. Fundamental aspects which must be considered in the design of new asphalt concrete pavements and overlays include the seasonal variations of the moisture content and the corresponding variations in the resilient modulus of the base, subbase and subgrade layers. In order to incorporate the seasonal variation of moisture into the pavement design process, the seasonal changes in the moduli of various pavement layers must be determined.
In situ structural properties of pavement layers vary on a seasonal basis. The degree to which seasonal variations in unbound pavement materials have been addressed in pavement design and evaluation, and the approaches taken to addressing them, are widely varied.
The laboratory investigations begin to explain why these variations occur, but do not, by themselves provide a complete basis for estimating the magnitude of the changes, and the duration of the different states, as is needed for design and evaluation. To address this issue fully, one must turn to field investigations. (Richter, 2006)
To quantify the seasonal variation of base or subgrade layers, seasonal adjustment factors are typically developed. Adjustment factors are determined by first obtaining sufficient resilient moduli to represent a geographic region for a yearly period. The moduli are normalized by selecting one of the seasons as a base value; usually a dry period (summer), when resilient modulus is the highest, is chosen.
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Another type of seasonal adjustment factors are those that are applied to deflection measurements. Many design procedures require a critical period maximum deflection. This corresponds to when the pavement is the weakest. If measurements are taken during a different period of the year, measurements require adjustment to the critical season.
The significant difference between pavement layer modulii values estimated from field data and the modulus values calculated from the material properties measured in the laboratory, may be related to differences in confining stress and the variability of subgrade materials in the field (Von Quintus and Killingsworth, 1998).
Another reason offered by Parker (1998) is that the stress level used in laboratory derived equations often do not truly represent the confining stress effects caused by overburden confinement, or horizontal residual confining stresses developed by traffic or compaction.
Yang (1988), Newcomb et al. (1989 ), Lindly and White (1989), Sebaaly et al.
(1994), and Uhlmeyer, et al. (1996) investigated the seasonal variations in deflections and in situ moduli for different unbound materials. Overall, the literature suggests that backcalculated base and subbase layer moduli tend to be less than the corresponding laboratory values, while moduli for subgrade layers tend to be greater than the lab values, though exceptions do occur.
Estimation of MR based on deflection data is usually accomplished by one of a variety of back-calculation methods. One approach is to use an algorithm that assumes layer moduli and then iterates to convergence with calculated stresses and strains at layer interfaces. Most of these back-calculation methods require input of seed values for layer moduli, and assumptions for the value of Poisson's Ratio for each material. A 8686
theoretical deflection basin is constructed from calculated deflections, and adjusted to fit the field measured deflection basin.
4.2 Characterizing moisture in MEPDG
The M-E Design Guide incorporates a predictive equation within the EICM to predict changes in modulus due to changes in moisture. The current MEPDG adopts the following model to predict the change of modulus due to a change in degree of saturation of the soils:
M b − a log R = a + (4-1) M Ropt ⎛ − b ⎞ 1+ EXP⎜ln + km.()S − Sopt ⎟ ⎝ a ⎠ where:
M /M Resilient modulus ratio; M is the resilient modulus at a given degree of R Ropt = R
saturation and M is the resilient modulus at a reference condition. Ropt
a = Minimum of log(M /M ). R Ropt
b = Maximum of log(M /M ). R Ropt
km = Regression parameter.
(S-Sopt) = Variation in degree of saturation expressed in decimal.
Figure 4.1 presents the correction factor for the moisture condition for the various degrees of saturation. One can observe that increase in moisture content decreases resilient modulus.
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3
2.5
2
subgrade 1.5 base
Correection Factor 1
0.5
0 -2.5 -1.5 -0.5 0.5 1.5 2.5 (S-Sopt)
Figure 4.1 Correction factor as a function of the degree of saturation.
The MEPDG suggests that the modulus ratio, MR/MRopt, is in the range of 2 to 0.5 for coarse-grained soils, while it is between 2.5 to 0.25 for fine-grained soils. This means that the fine-grained soils are more influenced by the moisture content than the coarse-grained soils. Generally, the degree of saturation of subgrades (especially for fine- grained subgrades) increases with time; the resilient modulus will decrease over the design period due to the increase in moisture content and reach the minimum resilient modulus.
4.3 Analysis of Collected Moisture Data
It is widely known that pavement subgrade soils not only experience temporary
(seasonal) changes in moisture content but also undergo changes in their long-term 8888
average annual moisture content. In this study, the variation of moisture within pavement layer was monitored by means of TDR moisture sensors, which measure the gravimetric moisture content. The gravimetric moisture content was generally recorded at the sites on a daily basis. The instrumentation layout is shown in section 3.2.1, whereas materials specifications are shown in section 3.2.2.
Figures 4.2-4.9 show the plot of the measured water content in the base, subbase and subgrade materials. These plots are separated into the driving lane and the passing lane to investigate if lane location would have made a difference.
Two observation points could be made: (a) the moisture content in the deeper portion of subgrade (180 cm from base surface) showed little variations over time, and
(b) precipitation did not seem to have a direct effect on daily water content variations in base and subbase layers.
Figure 4.2 Average daily water content measurements in Base Layer (Driving Lane)
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Figure 4.3 Average daily water content measurements in Base Layer (Passing Lane)
Figure 4.4 Average daily water content measurements in subbase Layer (Driving Lane)
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Figure 4.5 Average daily water content measurements in subbase Layer (Passing Lane)
Figure 4.6 Average daily water content measurements in top subgrade layer (top 30 cm of subgrade layer) Driving Lane.
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Figure 4.7 Average daily water content measurements in top subgrade layer (top 30 cm of subgrade layer) Passing Lane.
0.30
307-NJ 0.25
307-IA 0.20
307-CE 0.15 306-Cement
Gravimetric W.C Treated 0.10 ODOT 304 0.05 308-Asphalt Treated 0.00 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 date
Figure 4.8 Average daily water content measurements in bottom subgrade layer (at 180 cm from base surface) Driving Lane.
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0.24
307-NJ 0.22 307-IA
0.20 307-CE
0.18 306-Cement
Gravimetric W.C Treated
ODOT 304 0.16
308-Asphalt Treated 0.14 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 date
Figure 4.9 Average daily water content measurements in bottom subgrade layer (at 180 cm from base surface) Passing Lane
To further analyze the moisture data, the COV of each month is calculated as the ratio of the standard deviation of the moisture content for a month divided by the mean measured moisture content for the entire monitoring period. Figures 4.10-4.15 present the calculated monthly COV values, from which one can see that significant variations of
COV values occurred in the base and subbase materials. The COV values of the subgrade at shallow depth showed lower variation than those of base and subbase materials. Furthermore, based on the results presented in Figures 4.2-4.9, the ODOT 306-
Cement treated base materials showed the lowest monthly COV compared with the other base materials, particularly in subgrade at shallow depth. Similar to the ODOT 306-
Cement treated base section, the ODOT 308-Asphalt Treated base section also showed that the COV of the measured water content in the subgrade at shallow depth was relatively constant.
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The ODOT 304 and the unbound base materials (307-IA, 307-NJ, and 307-CE) showed significant water content variations in their base and subbase materials. However, since these sections showed only small variation in subgrade moisture content, one can still conclude that even though these materials were relatively slow in draining free water compared with the treated base materials sections, they still had the ability to drain the water out of the pavement system.
60 55 307-NJ 50 45
40 307-IA 35 30
COV % COV 25 307-CE 20 15 ODOT 10 304 5 0 S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- 03 03 03 03 04 04 04 04 04 04 04 04 04 04 04 04 05 05 05 05 05 05 05 05 05 05 05 05 06 date Figure 4.10 COV for moisture content in base with time (Driving Lane)
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60 55 307-NJ 50 45 40 307-IA 35 30
COV % 25 307-CE 20 15 ODOT 10 304 5 0 S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- 03 03 03 03 04 04 04 04 04 04 04 04 04 04 04 04 05 05 05 05 05 05 05 05 05 05 05 05 06 date
Figure 4.11 COV for moisture content in base with time (Passing Lane)
307-NJ 60 55 307-IA 50 45 40 307-CE 35
30 306- COV % 25 Cement 20 Treated ODOT 15 304 10 5 308- Asphalt 0 Treated S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- 03 03 03 03 04 04 04 04 04 04 04 04 04 04 04 04 05 05 05 05 05 05 05 05 05 05 05 05 06 date
Figure 4.12 COV for moisture content in subbase with time (Driving Lane)
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60 307-NJ 55
50 307-IA 45 40 307-CE 35 30
COV % COV 25 306- 20 Cement Treated 15 ODOT 10 304 5 0 308- S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- Asphalt 03 03 03 03 04 04 04 04 04 04 04 04 04 04 04 04 05 05 05 05 05 05 05 05 05 05 05 05 06 Treated date Figure 4.13 COV for moisture content in Subbase with time (Passing Lane)
50
45 307-NJ
40 307-IA 35
30 307-CE
25 306-Cement COV % COV 20 Treated
15 ODOT 304
10 308-Asphalt 5 Treated
0 S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- 03 03 03 03 04 04 04 04 04 04 04 04 04 04 04 04 05 05 05 05 05 05 05 05 05 05 05 05 06 date
Figure 4.14 COV for moisture content in top subgrade soil (top 30cm of subgrade layer) with time (Driving Lane)
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24 307-NJ 22 20 307-IA 18 16 307-CE 14 12 306-
COV % COV Cement 10 Treated 8 ODOT 304 6 4 308- Asphalt 2 Treated 0 S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- F- M- A- M- J- J- A- S- O- N- D- J- 03 03 03 03 04 04 04 04 04 04 04 04 04 04 04 04 05 05 05 05 05 05 05 05 05 05 05 05 06 date
Figure 4.15 COV for moisture content in top subgrade soil (top 30cm of subgrade layer) with time (Passing Lane)
Table 4.1 provides a summary of the months when the monthly COV exceeded the mean COV of the entire monitoring period. It can be seen that most of the months when the monthly COV values exceeded the mean COV values are in the beginning of the monitoring period. An initial sharp jump in the water content at the base, subbase and subgrade layers was observed in all sections due to heavy rainfall on the unprotected base surface, allowing the water to penetrate into the ground.
According to Table 4.1, the unbound materials, especially (ODOT 304), result in higher number of months where the monthly COV values exceeded the mean COV values when compared with the bound materials. Also, Table 4.1 shows that the subgrade at shallow depth shows a higher number of months that the monthly COV exceeds the mean COV.
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Table 4.1 Monthly moisture COV exceeded the mean COV Pavement Section 307-NJ 307-IA 307-CE Cement ODOT 304 Asphalt Layer Treated Treated P:Passing D. lane P. D. P. D. P. D. P. D. P. D. P. D:Driving lane lane Lane lane Lane lane Lane lane Lane lane Lane Oct Oct Oct Oct Oct Dec Dec Oct 03 03 03 03 03 03 ------03 03 ------Jan Jan Nov Dec Dec Jan Jan Nov 03 04 04 03 03 03 ------04 04 ------Feb Mar Jan Jan Feb Feb Dec 03 04 04 04 04 ------04 04 ------Mar Apr Feb Feb Mar Base course Jan 04 04 04 04 04 ------04 ------Apr May Apr Mar Jul Mat 04 04 04 05 04 ------05 ------Jun Apr Apr 04 04 05 ------Jul 04 ------Total 6 5 7 2 5 6 - - 5 3 - - Oct Oct Oct Oct Oct Oct Dec Dec Oct Oct Oct 03 03 03 03 03 03 03 03 03 03 03 Jan Feb Nov Dec Nov Nov Jan Jan Nov Jan Nov 03 04 04 03 03 03 03 04 04 03 04 Feb Mar Dec Jan Mar Dec Feb Feb Dec Feb Dec 03 04 04 03 04 04 03 04 04 03 04 Mar Apr Jan Feb Apr Jan Mar Apr Mar Jan 04 04 04 04 04 04 04 04 04 04 Apr Aug Mat May May Apr ODOT 304 Mar 04 04 04 04 05 04 04 subbase May Jan Apr Jun Jun Apr 04 04 05 04 05 04 Dec May May Jul May 04 05 05 04 05 Oct Aug 05 05 Dec 05 Total 7 ----- 7 9 7 4 4 4 8 3 6 5 Oct Oct Oct Oct Oct Oct Dec Dec Dec Jan Oct Oct 03 03 03 03 03 03 03 03 03 03 04 03 Nov Nov Jul Apr Jan Jan Jan Jan Feb Nov Nov 03 03 03 Jul 05 04 04 04 04 04 04 04 03 Dec Dec Aug May Feb Feb Feb Mar Dec Dec 03 03 03 05 04 04 Jul 05 04 04 04 03 May May Sep Jun Mar Aug May Mar Apr Jan Jan 04 04 04 05 04 04 05 04 04 04 04 Jun Jun Sep Apr Sep Jun Apr Oct Oct Subgrad Mat 04 04 04 05 04 05 04 04 05 05 e at Jul Jun Sep Oct Jul Nov Nov shallow Apr 04 04 05 04 05 04 Jul 05 05 05 depth Aug Jul Dec Nov Aug0 Aug May 04 04 05 05 05 4 05 Srp Aug Sep Sep Jun 04 04 05 04 05 Oct Sep Oct Oct 04 05 04 05 Mar0 Oct Dec 5 05 05 Total 8 10 10 4 3 5 7 7 11 9 6 6
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4.3.1 Variation of Moisture Content from Initial Water Content
Figures 4.16-4.21 shows the percent change in the measured moisture content
from the initial measured moisture content for different layers. Table 4.2 shows the
percent change in moisture content from initial moisture content in different seasons. In
calculating the initial water content the average of the first five measurements was used.
Percent change was calculated according to the following equation
⎛ max seasonal measured w.c - Initial measured w.c ⎞ Percent change % = ⎜ ⎟×100% (4-2) ⎝ Initial measured w.c ⎠
Table 4.2 Percent change in seasonal moisture content from initial moisture content Section 307-NJ 307-IA 307-CE Cement ODOT Asphalt Paveme Treated 304 Treated nt Layer Season D. P. D. P. D. P. D. P. D. P. D. P. lane lan lan Lan lan Lan lan Lan lan Lan lan Lan e e e e e e e e e e e Fall 132 15 5 6 4 1 ------92 80 ------Base Winter 68 12 18 8 50 18 ------33 4 ------course Spring 61 3 24 6 10 1 ------12 0 ------Summer 9 6 20 4 3 1 ------33 4 ------Fall 123 ---- 1 2 7 9 151 11 96 66 13 19 ODOT Winter 76 ---- 5 17 60 30 17 7 32 19 11 13 304 Spring 58 ---- 14 10 26 6 6 3 42 11 3 12 subbase Summer 6 ---- 8 1 14 0 6 4 59 5 8 13 Subgrad Fall 29 22 35 8 20 13 14 6 63 27 6 9 e at Winter 38 6 13 4 7 4 20 5 0 2 5 2 shallow Spring 58 24 18 2 3 16 6 3 15 3 1 1 depth Summer 5 4 25 15 4 5 14 6 13 5 3 1
99
220
200
180 307-NJ
160
140 307-IA 120
100
80 307-CE
60 Change in Gravimetric W.C % Gravimetric in Change 40 ODOT 304 20
0 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 date
Figure 4.16 Percent change in measured moisture content for base layer from initial water content (Driving Lane)
200 180 307-NJ 160 140 120 307-IA 100
80 307-CE 60
Change inGravimetric W.C % 40 ODOT 304 20 0 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 date
Figure 4.17 Percent change in measured moisture content for base layer from initial water content (Passing Lane)
100
210
190 307-NJ
170 307-IA 150
130 307-CE 110
90 306-Cement Treated 70 ODOT 304 50 Change in Gravimetric W.C % Change
30 308-Asphalt 10 Treated
-10 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 date
Figure 4.18 Percent change in measured moisture content of subbase from initial water content (Driving Lane)
100 90 307-NJ 80 307-IA 70 60 307-CE 50 306-Cement 40 Treated
30 ODOT 304 Change in Gravimetric W.C % Change 20 308-Asphalt 10 Treated 0 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 date
Figure 4.19 Percent change in measured moisture content of subbase from initial water content (Passing Lane)
101
110 307-NJ
307-IA 90
307-CE 70
306-Cement 50 Treated
ODOT 304 30 Change of Gravimetric % Change of W.C 308-Asphalt Treated 10
-109/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 date
Figure 4.20 Percent change in measured moisture content for top part subgrade (top 30 cm of subgrade layer) (Driving Lane)
100 307-NJ 80 307-IA
60 307-CE
40 306-Cement Treated
ODOT 304 20 Change of Gravimetric W.C % of Gravimetric Change 308-Asphalt 0 Treated 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 -20 date
Figure 4.21 Percent change in measured moisture content for top part subgrade (top 30 cm of subgrade layer) (Passing Lane)
According to the results shown in Figures 4.16-4.21 and Table 4.2, Fall and
Winter seasons are the two seasons that show the highest percent of variation comparing with other seasons. This Figure show that the water content in all materials showed an 102
increase from the initial water content. Change in the measured moisture content for the
subgrade at shallow depth is less than the change in moisture content measured in base
and subbase materials.
The ODOT 308-asphalt treated section and ODOT 306- cement treated base
section showed the least change in the water content compared with the other sections,
especially in the subgrade at shallow depth.
Among the unbound base materials ODOT 304 and ODOT 307 (307-IA, 307-NJ,
and 307-CE), ODOT 307-NJ showed the highest variation in water content comparing to
other sections. All sections showed an increase in moisture content after the freezing
period 2004; after that a more or less constant moisture was observed.
Table 4.3 shows the date at which equilibrium moisture content was reached.
Equilibrium moisture content is defined as the final moisture content after which no
significant variation in moisture content was observed.
Table 4.3 Date to reach equilibrium moisture content Section 307-NJ 307-IA 307-CE Cement ODOT Asphalt Pavement Treated 304 Treated Layer D. P. D. P. D. P. D. P. D. P. D. P. lane la lane Lane lane La lane Lan lane Lan lane Lan ne ne e e e Base Ma Jul --- Sep May May y Apr course 04 -- 04 04 04 04 ------04 ------ODOT Apr 04- 304 Aug --- Apr Jun Jul May Mar Apr Jul Jun May subbase 04 - 04 04 Jul 04 04 04 04 05 04 04 04 Subgrade at shallow Dec N. Dec May Sep Sep Sep Mar Sep Sep depth 04 P N.P 04 04 04 03 03 N.P. 04 03 03
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It can be seen from Table 4.3 that most sections reached an equilibrium moisture condition in the subgrade faster than in base and subbase materials.
Sections with bound base materials showed reaching equilibrium condition at earlier date than the sections with unbound materials.
For all sections, ODOT 304 section showed the least stable conditions, especially in the driving lane where no equilibrium water content was observed.
4.3.2 Variation of Moisture Content from Avg. water content
Figures 4.22-4.25 show the variation of measured moisture content from the mean measured moisture content. From these Figures one can see that significant variations occurred in the measured water content in the base, subbase and subgrade materials.
Initially, water content is lower than the average water content, with time it increased until exceeding the average water content while reaching the maximum at the freezing period of 2004. After that, the measured water content fluctuated around the mean water content for the rest of monitoring period.
Table 4.4 shows the percent change in moisture content from mean moisture content for different seasons. Percent change is calculated using the following equation:
⎛ max seasonal measured w.c.- mean measured w.c. ⎞ Percent change % = ⎜ ⎟×100% (4-3) ⎝ mean measured w.c. ⎠
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50 40 30 307-NJ 20 10 307-IA 0 -109/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 -20 307-CE -30 -40
Change in Gravimetric W.C % Change W.C in Gravimetric ODOT 304 -50 -60 date
Figure 4.22 Variation of moisture content in base from avg. water content
60 50 40 307-NJ 30 307-IA 20 10 307-CE 0 306-Cement 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 -10 Treated -20 ODOT 304
Change in Gravimetric W.C % -30 308-Asphalt -40 Treated -50 date
Figure 4.23 Variation of moisture content in subbase from avg. water content (Driving Lane)
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50 307-NJ 40 307-IA 30 307-CE 20 306-Cement Treated 10 ODOT 304 0 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 308-Asphalt Change % W.C of Gravimetric Treated -10
-20 date
Figure 4.24 Variation of Moisture Content in top part subgrade (top 30 cm of subgrade layer)
20 307-NJ
307-IA 10
307-CE
0 306-Cement Treated 9/1/03 1/1/04 5/1/04 9/1/04 1/1/05 5/1/05 9/1/05 1/1/06 ODOT 304 Change of Gravimetric % W.C of Change -10 308-Asphalt Treated
-20 date
Figure 4.25 Variation of Moisture Content in bottom part subgrade (at 185 cm from top of base layer)
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Table 4.4 Percent variation in moisture content from average moisture content for different seasons Section 307-NJ 307-IA 307-CE Cement ODOT Asphalt Paveme Treated 304 Treated nt Layer Season D. P. D. P. D. P. D. P. D. P. D. P. lane lan lan Lan lan Lan lan Lan lan Lan lan Lan e e e e e e e e e e e Fall 49 9 2 4 2 2 ------48 44 ------Base Winter 25 7 8 5 22 10 ------17 2 ------course Spring 23 2 11 4 4 0 ------6 0 ------Summer 3 4 9 2 1 1 ------17 2 ------Fall 56 ---- 1 2 4 6 83 7 49 40 36 7 ODOT Winter 34 ---- 3 13 30 20 10 4 16 11 10 9 304 Spring 26 ---- 10 8 13 4 3 2 21 7 21 2 subbase Summer 3 ---- 6 1 7 7 3 2 30 3 4 3 Subgrad Fall 29 15 20 6 12 10 14 13 48 22 12 11 e at Winter 38 4 8 3 4 3 19 11 4 2 11 3 shallow Spring 58 16 10 1 2 12 6 4 9 2 3 1 depth Summer 5 3 15 10 3 4 14 5 7 4 8 1
From Table 4.4 it can be seen that ODOT 304 section and ODOT 307-NJ section exhibit the highest percent of variation among all other sections. In most sections, subgrade materials showed the lowest variation, when compared to the base and subbase materials. The summer season showed the least variation among all other seasons.
4.3.3 Variation of Moisture Content with Depth from Season to Season
Figures 4.26-4.31 show the seasonal variation of moisture content with depth.
One can see that significant variations of the measured water content occurred in the base, subbase and subgrade materials from season to season. In all sections, the lowest water content measurements were observed in 2003-2004 seasons, which represent the initial monitoring period. With passage of time and after the measured water content had reached the equilibrium condition, small variation was observed. 107
Tables 4.5 and 4.6 show the percent of seasonal variation of moisture content from
yearly mean moisture content. Percent variation in these two tables is calculated using the
following equation:
⎛ seasonal measured w.c- mean yearly w.c.⎞ Percent variation % = ⎜ ⎟×100% (4-4) ⎝ mean yearly w.c. ⎠
Table 4.5 Percent seasonal variation of moisture content from mean moisture content for year 2004 Pavement Section Layer 307-NJ 307-IA 307-CE Cement ODOT 304 Asphalt Season Treated Treated D. P. D. P. D. P. D. P. D. P. D. P. lane lane lane Lane lane Lane lane Lane lane Lane lane Lane Fall -14.2 4.4 -2.7 0.0 16.8 6.7 ------16.7 -1.0 ------Base Winter -11.5 1.5 5.3 2.9 -2.3 1.6 ------2.3 2.2 ------course Spring 12.4 -3.1 2.0 0.0 -6.3 -3.2 ------6.0 2.5 ------Summer 12.9 -3.0 -4.0 -1.2 -7.2 -4.0 ------5.5 1.2 ------Fall -19.7 -0.6 ---- 2.4 20.2 13.7 -4.5 2.4 16.0 -7.6 -1.4 7.0 ODOT 304 Winter -12.2 0.3 ---- 3.7 6.4 -0.4 4.6 0.0 -1.1 -1.4 10.8 0.5 subbase Spring 15.5 0.9 ---- -1.8 -7.7 -5.3 2.3 -0.5 -6.2 6.8 -2.2 -3.1 Summer 16.4 -0.5 ---- -2.5 -2.7 -5.9 -2.6 -1.2 -6.0 6.5 -7.4 -2.7 Fall 6.0 -10.7 -8.4 -5.5 -4.2 -2.2 -7.9 -1.4 -13.0 -1.8 -6.8 -2.5 Subgrade Winter 18.1 8.8 5.7 -3.9 -0.6 7.5 -3.7 -0.8 0.5 -1.3 -2.8 -1.1 at shallow depth Spring -11.9 6.6 4.9 8.4 5.0 0.3 8.2 1.7 10.7 3.0 6.3 2.5 Summer -17.5 -2.9 -2.8 1.0 -0.4 -5.4 2.8 0.3 1.0 0.8 2.8 0.7
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Table 4.6 Percent seasonal variation of moisture content from mean moisture content for year 2005 Section 307-NJ 307-IA 307-CE Cement ODOT 304 Asphalt Pavement Season Treated Treated Layer D. P. D. P. D. P. D. P. D. P. D. P. lane lane lane Lane lane Lane lane Lane lane Lane lane Lane
Fall -1.3 -0.6 2.2 -0.2 -1.4 2.7 ------4.7 1.5 ------Winter -1.2 0.3 1.9 0.3 -2.0 1.9 ------0.8 0.9 ------Base course Spring 2.3 0.9 0.4 3.7 -0.5 -3.7 ------6.8 -0.8 ------Summer 0.7 -0.5 -3.9 -1.6 2.0 -2.3 ------1.4 -1.6 ------Fall -1.0 -0.6 ---- 1.9 -1.0 3.7 -8.6 0.6 -14.1 2.0 -1.4 -1.0 ODOT 304 Winter -1.3 0.3 ---- 1.3 -1.1 -1.0 -0.3 0.6 6.3 0.8 -2.1 -0.8 subbase Spring 1.9 0.9 ---- 2.2 5.7 -1.8 4.0 -0.6 11.3 -0.9 0.8 1.0 Summer 1.0 -0.5 ---- -3.8 -2.2 -1.1 3.6 -0.4 -5.8 -1.9 4.8 1.8 Fall -1.1 -10.7 -5.4 0.9 -4.1 -3.2 -8.5 -4.1 -0.8 -3.9 -2.7 -1.2 Subgrade Winter -1.2 8.8 -4.7 0.0 -0.7 -0.2 -3.0 -2.6 0.8 -1.9 -4.2 -1.5 at shallow depth Spring 2.1 6.6 13.4 1.2 0.6 0.6 5.0 3.7 2.7 4.4 0.2 0.5 Summer 0.9 -2.9 5.8 -0.8 2.9 2.9 4.9 2.5 -1.9 1.8 8.9 3.3
Negative values in these two tables mean that the seasonal moisture content is
lower than the average yearly moisture content. For each year shown in this table, the
starting date is Dec. 21 of previous year and the ending date is Dec. 20 of the current
year.
Tables 4.5 and 4.6 show that in most sections, moisture content measurements in
driving lanes exhibit higher percent of variation when compared to those in the passing
lanes. In most sections, the subgrade at the shallow depth exhibits lower variation when
compared to the base and subbase materials.
Year 2004 shows higher percent of variation when compared to 2005. This is
likely due to the fact that an equilibrium moisture content was reached in most sections in
2005.
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ODOT307-NJ avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0
20
40
60
80
100
Depth (cm) 120 fall 2003
140 fall 2004
160 fall 2005
180 AVG 200
(a) ODOT 307-NJ avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0
20
40
60
80
100
Depth (cm) 120 winter 2003
140 winter 2004
160 winter 2005
180 AVG 200
(b) Figure 4.26 Avg. moisture content with depth in cement treated base section (Driving Lane)
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ODOT307-NJ avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0
20
40
60
80
100 Depth (cm) 120 spring 2004
140 spring 2005 160
180 spring 2006
200
(c) ODOT 307-NJ avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0
20
40
60
80
100
Depth (cm) summer 120 2004
140 summer 2005 160 summer 2006 180 AVG
200
(d) Figure 4.26 Avg. moisture content with depth in cement treated base section (Driving Lane) continued
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ODOT307-IA avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100
Depth (cm) 120 fall 2003
140 fall 2004
160 fall 2005
180 Avg 200
(a) ODOT 307-IA avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100
Depth (cm) winter 120 2003
140 winter 2004 160 winter 2005 180 Avg
200
(b) Figure 4.27 Avg. moisture content with depth in ODOT 307-IA base section (Driving Lane)
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ODOT307-IA avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100 spring Depth (cm) 120 2004 spring 140 2005
160 spring 2006 180 Avg
200
(c) ODOT 307-IA avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100 summer Depth (cm) Depth 120 2004 140 summer 160 2005
180 AVG
200
(d) Figure 4.27 Avg. moisture content with depth in ODOT 307-IA base section (Driving Lane) continued
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ODOT307-CE avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0
20
40
60
80
100
Depth (cm) Depth 120 fall 2003
140 fall 2004
160 fall 2005
180 Avg 200
(a) ODOT 307-CE avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0
20
40
60
80
100
Depth (cm) Depth winter 120 2003
140 winter 2004 160 winter 2005 180 Avg 200
(b) Figure 4.28 Avg. moisture content with depth in ODOT 307-CE base section (Driving Lane) 114
ODOT307-CE avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0
20
40
60
80
100 spring Depth (cm) Depth 120 2004 spring 140 2005 160 spring 2006 180 Avg
200
(c) ODOT 307-CE avg moisture content with depth Moisture Content 0 0.050.10.150.20.250.30.35 0
20
40
60
80
100
Depth (cm) 120 summer 140 2004 summer 160 2005 180 AVG
200 (d) Figure 4.28 Avg. moisture content with depth in ODOT 307-CE base section (Driving Lane) conbtinued
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Cement Treated-moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80 fall 2003 100 fall 2004 Depth (cm) 120 fall 2005 140
160 average
180
200
(a) cement treated -avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100 winter Depth (cm) Depth 120 2003
140 winter 2004 160 winter 2005 180 Avg. 200
(b) Figure 4.29 Avg. moisture content with depth in cement treated base section (Driving Lane)
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cement treated -avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100
Depth (cm) Depth 120 spring 2004
140 spring 2005 160
180 spring 2006 200
(c) Cement Treated-moisture content with depth Moisture Content 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100
Depth (cm) Depth 120 summer 2004 140 summer 160 2005
180 average
200 (d) Figure 4.29 Avg. moisture content with depth in cement treated base section (Driving Lane) continued
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ODOT 304-moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100 Depth (cm) Depth 120 fall 2004
140 fall 2005 160
180 average 200
(a) ODOT 304-moisture content with depth Moisture Content 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100 winter Depth (cm) Depth 120 2003
140 winter 2004 160 winter 2005 180 Averege
200
(b) Figure 4.30 Avg. moisture content with depth in ODOT 304 base section (Driving Lane) 118
ODOT 304-avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0
20
40
60
80
100
Depth (cm) Depth 120 spring 2004
140 spring 2005 160
180 spring 2006 200
(c) ODOT 304-moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0
20
40
60
80
100
Depth (cm) summer 120 2004 140 summer 2005 160
180 average
200
(d) Figure 4.30 Avg. moisture content with depth in ODOT 304 base section (Driving Lane) continued 119
Asphalt Treated-moisture content with depth Moisture Content 0.05 0.1 0.15 0.2 0.25 0 fall 2003 20
40 fall 2004
60 fall 2005
80 average 100
Depth (cm) Depth 120
140
160
180
200
(a) Asphalt Treated - avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0 winter 20 2003 40 winter 2004 60 winter 2005 80 avg 100
Depth (cm) Depth 120
140
160
180
200 (b) Figure 4.31 Avg. moisture content with depth in Asphalt treated base section (Driving Lane) 120
Asphalt treated -avg moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0 spring 20 2004 spring 40 2005 60 spring 2006 80 avg 100
Depth (cm) Depth 120
140
160
180
200
(c) Asphalt Treated-moisture content with depth Moisture Content 0 0.05 0.1 0.15 0.2 0.25 0 summer 20 2004 40 summer 2005 60
80 average
100
Depth (cm) Depth 120
140
160
180
200
(d) Figure 4.31 Avg. moisture content with depth in Asphalt treated base section (Driving Lane) continued 121
4.4 Impact of Permeable Base Materials on the Pavement Structural Capacity
Deflection-based non-destructive testing (NDT) techniques have been widely used for evaluation of the structural integrity and the elastic Moduli of pavement structures. FWD is the primary deflection measuring instrument currently used by the
Ohio Department of Transportation (ODOT). Because of its versatility, reliability, and ease of use, Dynatest FWD is being used in this research project. The stiffness of different pavement layers was compared based on the maximum normalized deflection
(mils/Kips) measured at the center of FWD loading plate.
The maximum measured deflection, at the center of FWD loading plate, can give an indication about the composite pavement layer strength and stability. Figure 4.32 shows that the measured deflection at pavement section with bound base layer are less than the other sections having unbound base layer. Pavement section with ODOT 306 cement and 308 Asphalt treated materials are stronger than sections with ODOT 304 and
307 granular materials. Consequently, reduction of pavement thickness is very likely for pavements with bound base materials.
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307 - IA 307 - NJ 307 - CE C.T.B ODOT 304 A.T.B 0.7
0.65
0.6
0.55 2003 0.5
0.45 2004
0.4 2005 0.35
0.3 Normilized deflection (mils/kips)
0.25
0.2
Figure 4.32 Maximum normalized deflection measured at the center of the FWD load plate
4.5 Comparison of Field Backcalculated and Laboratory Determined Moduli
EverCalc program was used for backcalculations of moduli of pavement layer. In this research, the backcalculation for each pavement layer modulus was done in addition to the laboratory measured moduli (Liang, 2007).
Table 4.7 shows the comparison between the laboratory measured (Liang 2007) and EverCalc backcalculated moduli for different pavement layers and base course materials. The backcalculations were performed for the measured surface course deflections in different dates: on the newly constructed pavement with no traffic open at that time, and after opening the traffic on the pavement sections.
For the unbound base materials, ODOT 304 section had the lowest modulus in both center and wheel path lines; whereas, the highest moduli were backcalculated for the
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307-NJ and 307-CE materials, in both center and wheel path lines. The 307-IA moduli were in a range somewhere in between. On the other hand, the moduli for the bound asphalt and cement treated materials were higher than the unbound materials. The
Cement Treated base modulus was the highest among all base materials.
Table 4.7 Laboratory and EverCalc backcalculations comparison for pavement layer moduli (ksi) for both sites. Materials Laboratory Backcalculated Avg. results moduli ksi Backcalculated ksi moduli ksi Asphalt concrete mixture --- 750-1500 1040 ATB 100-154 214-318 286 CTB 224-366 243-408 360 ODOT 304 base 24-81 29-128 74 307-NJ 31-97 52-185 121 307-IA 28-95 49-178 115 307-CE 32-104 55-184 114 A-6 subgrade 5-8 17-27 21
Results show that backcalculated resilient modulus for all base and subgrade material show higher modulus compare to laboratory measured modulus. This is match with the result obtained by Liang (2007) where the bound base materials possess higher resilient modulus than unbound base materials.
4.6 Estimation of Subgrade Seasonal Adjustment Factor (SAF)
The SAF can be defined as the material modulus for a certain pavement section at any season divided by the modulus during a reference season. To estimate the seasonal
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and/or monthly variation in the subgrade elastic modulus and calculate the corresponding
SAF for each of the different permeable base materials, the following steps were followed:
1- The measured moisture contents were averaged for each month and season.
2- Equation 1 can be used to estimate the resilient modulus ratio (M /M ) at each R Ropt
month and season.
3- The month of August was selected as the reference month because it show the lowest
monthly modulus ratio among all other months. The monthly adjustment factor for
the other months were then calculated by dividing the modulus ratio at each month
by August modulus ratio.
Table 4.8 subgrade monthly adjustment factor Month Subgrade Moduli January 1.26 February 1.23 March 1.18 April 1.16 May 1.12 June 1.08 July 1.07 August 1.00 September 1.06 October 1.09 November 1.18 December 1.19 4-Applying the previous steps (1 to 3) to the seasonal data gives the following seasonal adjustment factors. Summer was considered the reference season. Table 4.9 subgrade seasonal adjustment factor Month Subgrade Moduli Winter 1.17 spring 1.07 summer 1.00 Fall 1.11
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5- The subgrade modulus at each month and/or season can then be calculated by multiplying the SAF, by the measured backcalculated moduli if laboratory measurement is not available.
4.7 Multilayer Elastic Analysis
The tensile strain at the bottom of the AC layer and the compressive strain at the top of the subgrade soil, for all sections, were computed using the Multi Linear Elastic
Analysis (MLEA). The strains were computed based on the backcalculated layers' moduli, and assuming the standard 9,000 lb FWD load with 80 psi pressure.
Figures 4.33 and 4.34 show the computed tensile and compressive strains for different permeable base materials, respectively. Figure 4.33 indicates that the tensile stains are slightly smaller when using bound base materials rather than unbound granular bases. On the other hand, Figure 4.34 shows that the compressive strain at the top of subgrade layer is highly reduced when using the bound base layer. The reason is that the contribution of the bound base modulus is greater when calculating the compressive strain on the surface of the subgrade, while the AC tensile strains are mainly affected by the AC modulus which is almost the same for both sections.
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3.5E-05
3.0E-05
2.5E-05
2.0E-05
1.5E-05
1.0E-05
Tensile Strains at bottom of ac layer at bottom Strains Tensile 5.0E-06
0.0E+00 304 307-IA 307-NJ 307-CE Asphalt Cement Treated Treated Material Type
Figure 4.33 Computed Tensile Strains for different permeable base materials
Vertical compressive strain 0.E+00 1.E-05 2.E-05 3.E-05 4.E-05 5.E-05 6.E-05 7.E-05 0
5
Asphalt Treated 10 Cement Treated AC layer 307-CE 307-NJ 307-IA 304 15
base layer
20 subbase layer Depth from the surface (in)
25
Rockfill layer
30 Subgrade layer
35
Figure 4.34 Computed Compressive Strains for different permeable base materials
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4.8 Prediction of Pavement Service Life
The Mechanistic-empirical design methods (MEPDG 2004) for flexible pavements were based on the assumption that the pavement life is inversely proportional to the magnitude of the traffic-induced pavement strains (MEPDG 2004). Two competing failure mechanisms were typically assumed related to the pavement design. These two failure mechanisms are the cracking due to fatigue of the asphalt bound pavement layers and the rutting due to accumulated permanent deformations at the top of subgrade soil.
To characterize the fatigue damage in asphalt layer, numerous model forms can be found in the existing literature (MEPDG 2004). The most commonly used model form to predict the number of load repetitions to fatigue cracking is a function of the tensile strain and mix stiffness (modulus). The performance model for fatigue cracking considered in this analysis was that included in the MEPDG. For fatigue cracking, the design guide suggested the following performance model.
3.9492 ⎛ 1 ⎞ 1 1.281 ⎜ ⎟ ⎛ ⎞ N f = 0.00432× k'1×C⎜ ⎟ ⎜ ⎟ (4-5) ⎝ ε t ⎠ ⎝ E ⎠ C = 10M
⎛ Vb ⎞ M = 4.84⎜ − 0.69⎟ ⎝Va +Vb ⎠ For the bottom-up cracking 1 k' 1 = 0.003602 0.000398 + 1+ e (11.02 − 3.49 × hac) where:
Nf = number of repetitions to fatigue cracking.
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εt = tensile strain at the critical location.
E= The elastic modulus of the asphalt mixture
Vb = effective binder content (%).
Va = air voids (%).
hac= thickness of AC layer
The rutting model incorporated in the Asphalt Institute design manual is used for prediction of permenant deformation as follow:
-9 -4.477 Nf= 1.365 × 10 εc (4-6) where,
Nf = Number of load repetitions to failure
εc = Compressive strain at the top of the subgrade
The predicted pavement life (number of repetitions to failure) is considered the lower of the number of repetitions to failure obtained from either the fatigue or the rutting models.
Figures 4.35 and 4.36 shows the predicted pavement life, in ESALs, for all sections considering both fatigue cracking and permanent deformation. Figure 4.35 shows that bound base materials resulted in greater predicted pavement life than unbound base materials. ODOT 306 Cement treated base material show the highest predicted life among all permeable base materials. For example, pavement rutting prediction service life indicates that the rutting life is greatly increased (about 3 times) when using cement treated compare to the ODOT 304 base material. In general, calculated service life is more critical to fatigue cracking than permanent deformation pavement performance.
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1.2E+12
1.0E+12 s
8.0E+11
6.0E+11
4.0E+11 Rutting Service Life, ESAL
2.0E+11
0.0E+00 304 307-IA 307-NJ 307-CE Asphalt Cement Treated Treated Material Type
Figure 4.35 Predicted pavement rutting lives in ESALs for different permeable base materials
1.8E+10
1.6E+10
1.4E+10 s
1.2E+10
1.0E+10
8.0E+09
6.0E+09 Fatique Service Life, Life, Service ESAL Fatique 4.0E+09
2.0E+09
0.0E+00 304 307-IA 307-NJ 307-CE Asphalt Cement Treated Treated Material Type
Figure 4.35 Predicted pavement fatigue lives in ESALs for different permeable base materials
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4.9 Conclusions
The performance of different permeable base materials used by Ohio Department of Transportation (ODOT) was evaluated in this study. Moisture content variation was monitored to determine the impact of using different permeable base materials on variation of subgrade moisture content. The moisture contents measured at ATB-90 site showed long-term equilibrium with only a small seasonal fluctuation. The seasonal variation in subgrade moisture was observed only at shallow depths. At deeper depths, there was no significant difference in the moisture content, where the moisture reaches equilibrium. Precipitation did not seem to have a direct effect on daily water content variations in base and subbase layers.
A Comparison between the backcalculated and laboratory determined moduli was presented for different base course materials and for subgrade soils. The comparison showed a significant discrepancy between backcalculated and laboratory measured moduli. Nevertheless, the general trend of the stiffness of the base course material was the same according to lab-measured and backcalculated field moduli.
Seasonal adjustment factors for the subgrade soil were estimated based on the collected moisture data at ATB-90 project. These factors could be used to quantify the effect of moisture content variation on pavement performance.
Open graded permeable base materials show a high contribution to the structure support of the pavement system and results in great increase in the pavement service lives. The performance analysis of the pavement sections having bound base layers are stronger than the other sections with unbound base materials. The predicted rutting life,
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for the pavement sections with cement treated, was up to 3 times greater than the other sections with unbound base materials.
Based on both field monitoring and laboratory test results, ODOT 306-Cement treated base materials exhibit the best ability to drain out the infiltrated water than the other base materials used in this research project.
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CHAPTER V
EVALUATION OF ENHANCED INTEGRATED CLIMATIC MODEL PREDICTION
OVER DIFFERENT PERMEABLE BASE MATERIALS.
This chapter focuses on evaluating the capability of the EICM to predict the moisture, temperature and frost action profiles in a pavement system. Weather data obtained from an automated weather station, such as precipitation, temperature, wind speed and percent sunshine, is used as input in the model. Materials properties of six different base materials, subbase and subgrade are also entered as a part of the model materials input. For this study, the EICM 3.02 (August 2004) version is used for predictions.
5.1 Importance of Climate in Mechanistic-Empirical Design
Environmental conditions have been found to exert significant impact on the performance of flexible pavements. External factors such as precipitation, temperature, freeze-thaw cycles, and depth to water table are the main environmental factors that have exerted major influences on the pavement performance. The Internal factors, such as the susceptibility of the pavement materials to moisture and freeze-thaw damage, infiltration potential of the pavement, control the extent to which the pavement will react to the
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applied external environmental conditions. The Mechanistic Empirical Design Approach fully considers the change of temperature and moisture profiles in the pavement structure and subgrade over the design life of a pavement, through the use of a climatic modeling software referred to as the Enhanced Integrated Climatic Model (EICM), (MEPDG,
2004).
Moisture and temperature are the two environmentally driven variables that can significantly affect the pavement layer and subgrade properties as well as the load carrying capacity. Some of the effects of environment condition on the pavement base materials used in this study are enumerated below:
-Asphalt stabilized base materials have been found to possess stiffness three times higher than typically compacted unbound granular base materials. However, laboratory test results (Ashteyat, 2004) clearly indicated a strong dependency of the strength and stiffness of asphalt stabilized materials on the temperature. A considerable reduction in the strength and stiffness was observed in this material when temperature was increased.
Also, the asphalt stabilized material shows a tendency for bond loss due to moisture and freezing thawing cycles, leading to high probability of pre-mature pavement failure
(Liang, 2007).
- Cement stabilized granular base materials properties showed that both flexural strength and resilient moduli are not significantly affected by normal temperature changes.
- In general, the higher the moisture content, the lower the modulus of unbound granular base materials. However, moisture has two separate effects. First, moisture can affect the state of stresses, through suction or pore water pressure. Coarse grained and fine-grained materials can exhibit more than a fivefold increase in modulus due to the soils being
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drying out. The moduli of cohesive soils are affected by a complex clay-water- electrolyte interaction. Second, moisture can affect the structure of the soil through destruction of the cementation between soil particles (Design Guide).
- Bound granular base materials are not directly affected by the presence of moisture.
However, excessive moisture can lead to stripping in asphalt stabilized mixtures. Cement bound granular materials may also be damaged during freeze-thaw and wet-dry cycles, as reflected in modulus reduction. It was found that a considerable reduction in the compressive strength of cement treated materials after being subjected to 15 cycles of freezing/thawing cycles. The amount of unconfined compressive strength reduction of cement stabilized granular base materials due to freezing/thawing was found to be up to
60% at 35 cycles (Liang, 2007).
5.2 The Enhanced Integrated Climatic Model (EICM)
The EICM is a one-dimensional coupled heat and moisture flow computer program that simulates changes in the behavior and characteristics of pavement and subgrade materials in conjunction with climatic conditions over several years of operation. The EICM consists of three major components:
• The Climatic-Materials-Structural Model (CMS Model) developed at the University of
Illinois.
• The CRREL Frost Heave and Thaw Settlement Model (CRREL Model) developed
at the United States Army Cold Regions Research and Engineering Laboratory
(CRREL).
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• The Infiltration and Drainage Model (ID Model) developed at Texas A&M
University.
The original version of the EICM, referred to simply as the Integrated Climatic
Model, was developed for the Federal Highway Administration (FHWA) at Texas A&M
University, Texas Transportation Institute in 1989. The current EICM (version 3.02) computes and predicts the following information for the entire pavement/subgrade profile: temperature, resilient modulus adjustment factors, pore water pressure, water content, frost and thaw depths, frost heave, and drainage performance.
5.2.1 Incorporation of EICM into the Design Guide
Climate is fully incorporated into the Design Guide methodology by incorporating the EICM software as an integral part of the Mechanistic Empirical Design Guide procedure. The user specified inputs to the EICM are entered through interfaces provided as part of the Design Guide software. The EICM processes these input and feeds the computer output to the three major components of the Design Guide’s mechanistic- empirical design framework-materials, structural responses, and performance prediction.
The most important output required from the EICM for the flexible and rigid pavement design is a set of adjustment factors for unbound material layers to account for the effects of environmental parameters and conditions such as moisture content changes, freezing, thawing, and recovery from thawing. Furthermore, EICM can compute in-situ temperatures at the midpoints of each bound sublayer as well as the temperature profiles within the AC and/or PCC layer for every hour, and the average moisture content for each sublayer in the pavement structure.
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Birgisson et al. (2000) provided detailed comparisons between field measurements and predictions obtained from ICM, in terms of seasonal variations in temperature, moisture content, and layer moduli, at two representative flexible pavement test sections at the Minnesota Research Project (Mn/ROAD) site. The trend of the predicted moisture content was shown to correspond well to the measured, except during spring thaw when the ICM does not account for the critical increase in volumetric moisture content.
Richter and Witczak (2001) used two versions of the ICM in evaluation the moisture prediction capabilities of the Integrated Climatic Model (ICM), Version 2.1 and
2.6, using data collected as a part of the Long Term Pavement Performance Program
(LTPP) Seasonal Monitoring Program (SMP). It was found that the agreement between monitored moisture contents and those predicted using ICM Version 2.1 was poor. On the other hand, an overall agreement was observed between the monitored moisture contents and those predicted with ICM Version 2.6.
Hydenger (2003) analyzed data from SMP testing at a test site in Ohio for the seasonal variations of moisture and temperature. The analyses were performed using daily averages for air and soil temperatures and monthly measurements of soil volumetric water content. It was found that subgrade resilient modulus varies seasonally due to changes in moisture content. Subgrade resilient modulus decreases with increase in moisture content.
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5.3 Using TDR to Measure Moisture Content and Dry Density
It was recognized that significant relationship exists between dielectric properties of soils and their moisture content. The sensitivity of the TDR probe to changes in the dielectric constant of the soils between the two conductors (rods) was the underlying principle for the TDR to monitor in-situ soil moisture content. The large contrast between the dielectric constant of free water and dry soils potentially makes TDR an effective in- situ measuring technique to monitor unsaturated soils moisture content.
An electromagnetic wave is transmitted along a coaxial cable rod that acts as a wave-guide, and through the surrounding medium. The velocity of the pulse through the medium is influenced by the dielectric constant (Ka) of the material. Many studies have developed empirical relationships between the dielectric constant and the water content.
Siddiqui-Drnevich (1995) equation shown below was found to be one of the most reliable relationships
1 ρ w θ g = [ k a − a] (5-1) b ρ d
where θg is the gravimetric water content,
b and a are soil-dependent calibration constants,
3 ρd is the dry soil density (g/cm ), and
3 ρw is the density of water which is approximately equal to 1.0 (g/cm ).
Siddiqui-Drnevich calibration equation, also referred to as Purdue method, accounts for both soil density and soil type (Drnevich et al. 2002).
138
Recently, Al-Akhras (2004) conducted a specific laboratory calibration of the
TDR probes for the base, subbase and subgrade materials taken from the ATB-90 project sites. Appropriate calibration coefficients (a and b) for the studied materials in this research were obtained.
In order to calculate the in-situ gravimetric water content using Equation (5-1), it is crucial to know the in-situ soil density surrounding each installed TDR probe. The field density was expected to vary from one section to another and even within the same section but in different depths, because the soil was backfilled manually during the instrumentation process.
Initial dry density of the backfilled soil around the TDR probe can be calculated
3 by solving Equation (5-1) for ρd, with the density of water considered as ρw =1.0 (g/cm )
1 a ρ d = [ K a − ] (5-2) b θ g b
Equation (5-2) includes two soil-dependent parameters (a and b) which were determined from the laboratory calibration studies for each material type. The value
of Ka was measured from the TDR output immediately after each probe was installed
at the site. The value of θ g can be determined in the laboratory using the oven-dry method. In this way, initial in-situ dry density of the soils surrounding the TDR probe can be more accurately determined for each TDR probe embedded at the project sites (Al-
Akhras, 2004).
The instrumentation layout is shown in section 3.2.1, whereas materials specifications are shown in section 3.2.2. Table 5.1 shows the materials properties used in
EICM for representing the permeable base layers.
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Table 5.1 Properties of Permeable Bases Materials used in the model.
(a) Asphalt Cement Materials properties Range used Properties ------15.25 Thickness of layer (in) Thermal conductivity asphalt 0.44 to 0.81 0.63 Btu/(ft)(hr)(°F). shortwave absorptivity for fresh asphalt 0.9-0.98 0.95 (black) 0.22 to 0.40 0.32 heat capacity of asphalt Btu/(lb)( °F) ------148 total unit weight of asphalt (pcf)
(b)Material Properties (Base layer) cement Asphalt Property 307-NJ 307-IA 307-CE Treated 304 Treated Thickness 4 4 6 4 4 4 Porosity 0.22 0.19 0.23 0.37 0.16 0.37 Specific Gravity 2.6 2.6 2.6 2.6 2.59 2.6 Saturated permeability 159 95 154 1044 59 1056 (ft/hr) 127 128 127 109 130 108 dry unit weight Percent passing 47 40 28 5 45 5 #4 sieve % 0 0 0 0 0 0 plasticity index Percent passing 1.2 0.7 1.18 0.5 6.5 0.5 #200 sieve % diameter D60 12.2 11.9 16.1 16 11.9 16 (mm) initial volumetric 17.9 6.7 6.8 8 11.25 8.3 water content
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Table 5.1 Properties of Permeable Bases Materials used in the model, (c) Material Properties ( Subbase layer), continued Cement Asphalt property 307-NJ 307-IA 307-CE Treated 304 Treated thickness 12 12 12 12 12 12 porosity 0.3 0.3 0.3 0.3 0.3 0.3 Specific gravity 2.6 2.6 2.6 2.6 2.6 2.6 saturated 226 226 226 226 226 226 permeability (ft/hr) Dry unit weight 122 122 122 122 122 122
Percent passing #4 33 33 33 33 33 33 sieve % plasticity index 0 0 0 0 0 0
Percent passing 4.4 4.4 4.4 4.4 4.4 4.4 #200 sieve % diameter D60 (mm) 22.4 22.4 22.4 22.4 22.4 22.4 initial volumetric 12.65 10.7 8.6 18 12 18 water content
(d) Material Properties ( Subgrade layer), continued Cement Asphalt Property 307-NJ 307-IA 307-CE Treated 304 Treated Thickness 210 210 210 210 210 210 Porosity 0.38 0.38 0.38 0.38 0.38 0.38 Specific gravity 2.75 2.75 2.75 2.75 2.75 2.75
Saturated 1.2x10-05 1.2x10-05 1.2x10-05 1.2x10-05 1.2x10-05 1.2x10-05 permeability (ft/hr) Dry unit weight 97 97 97 97 97 97 Percent passing #4 94 94 94 94 94 94 sieve % Plasticity index 13 13 13 13 13 13
Percent passing 69 69 69 69 69 69 #200 sieve % 0.056 0.056 0.056 0.056 0.056 0.056 diameter D60 (mm) initial volumetric 25 28 25 32 32 25 water content 141
5.4 Evaluation Results
Comparisons between the EICM-predicted and field-measured data were carried out for the time interval between the beginning of the project (October 2003) up to June
2006
5.4.1 Temperature Data Comparison
Comparisons between the EICM-predicted and field-measured temperature profiles were carried out for the time interval between the beginning of the project
(October 2003) up to June 2006. The measured temperature profiles are compared with the predicted in Figures 5.1 to 5.6 for six different pavement sections built with six studied base materials at site II. These figures illustrate that EICM-predicted temperature profiles compare marginally with the actual measured temperature profiles. A larger difference was found in the predicted asphalt layer temperature, even though the actual air temperature and the field measured temperatures within the asphalt concrete layer were used as inputs in the model. The predicted temperatures within the asphalt layers were greater than those measured in the field. Going deeper into the ground, the difference between the measured and predicted temperature became relatively small.
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ODOT 307-NJ ODOT 307-NJ
Average 4 p.m Temperature, July 2004 (F) Average 4 p.m Temperature, Oct 2004 (F) 40 60 80 100 120 40 50 60 70 80 90 0 0
10 10
20 20
30 30 40 40 50 50 Depth (in) Depth (in) 60 60 70
70 80 Field Measured Field Measured EICM 3.02 EICM 3.02 80 90
90 100
ODOT 307-NJ ODOT 307-NJ
Average 4 p.m Temperature, Jan 2005(F) Average 4 p.m Temperature, April 2005 (F) 15 25 35 45 55 15 35 55 75 95 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80 EICM 3.02 Field Measured Field Measured EICM 3.02 90 90
100 100
Figure 5.1 Temperature comparisons for 307-NJ section.
143
ODOT 307-IA ODOT 307-IA
Average 4 p.m Temperature, July 2004 (F) Average 4 p.m Temperature, Oct 2004 (F) 40 60 80 100 120 40 50 60 70 80 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80 Field Measured Field Measured EICM 3.02 EICM 3.02 90 90
100 100
307-IA ODOT 307-IA
Average 4 p.m. Temperature, Jan 2005 (F) Average 4 p.m. Temperature, April 2005 (F) 15 25 35 45 55 15 35 55 75 95 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80 EICM 3.02 Field Measured Field Measured EICM 3.02 90 90
100 100
Figure 5.2 Temperature Comparisons for 307-IA section
144
ODOT 307-CE ODOT 307-CE
Average 4 p.m. Temperature, July 2004 (F) Average 4 p.m. Temperature, Oct 2004 (F) 40 60 80 100 120 40 50 60 70 80 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 Field Measured 70 EICM 3.02 80 80 Field Measured EICM 3.02 90 90
100 100
ODOT 307-CE ODOT 307-CE
Average 4 p.m. Temperature, Jan 2005 (F) Average 4 p.m Temperature, April 2005 (F) 15 25 35 45 55 15 35 55 75 95 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80 EICM 3.02 Field Measured 90 90 Field Measured EICM 3.02 100 100
Figure 5.3 Temperature Comparisons for 307-CE section
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ODOT 306-Cement Treated ODOT 306- Cement Trated
Average 4 p.m Temperature, July 2004 (F) Average 4 p.m Temperature, Oct 2004 (F) 40 60 80 100 120 40 50 60 70 80 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80 Field Measured EICM 3.02 90 Field Measured 90 EICM 3.02 100 100
ODOT-306 Cement Treated ODOT 306-Cement Treated
Average 4 p.m Temperature, Jan 2005 (F) Average 4pm Temperature, April 2005 (F) 15 25 35 45 55 15 35 55 75 95 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80 EICM 3.02 Field Measured Field Measured EICM 3.02 90 90
100 100
Figure 5.4 Temperature Comparisons for ODOT 306-Cement Treated section.
146
ODOT-304 ODOT-304 Average 4 p.m. Temperature, July 2004 (F) Average 4 p.m. Temperature, Oct 2004 (F) 40 60 80 100 120 40 50 60 70 80 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) 60 60
70 70
Depth Below PavementDepth Below Surface (in) Field Measured 80 80 Field Measured EICM 3.02 90 EICM 3.02 90
100 100
ODOT-304 ODOT-304 Average 4 p.m. Temperature, Jan 2005 (F) Average 4 p.m. Temperature, April 2005 (F) 15 25 35 45 55 15 35 55 75 95 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80 EICM 3.02 Field Measured Field Measured EICM 3.02 90 90
100 100
Figure 5.5 Temperature Comparisons for ODOT-304 section
147
ODOT308-Asphalt Treated ODOT308-Asphalt Trated
Average 4 p.m. Temperature, July 2004 (F) Average 4 p.m. Temperature, Oct 2004 (F) 40 60 80 100 120 40 50 60 70 80 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80 Field Measured Field Measured EICM 3.02 EICM 3.02 90 90
100 100
ODOT 308-Asphalt Treated ODOT Asphalt Treated
Average 4 p.m. Temperature, Jan 2005 (F) Average 4 p.m. Temperature, April 2005 (F) 15 25 35 45 55 15 25 35 45 55 65 75 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80 EICM 3.02 Field Measured Field Measured EICM 3.02 90 90
100 100
Figure 5.6 Temperature Comparisons for ODOT 308-Asphalt Treated section.
148
5.4.2 Frost Depth Comparison
Comparisons between the EICM-predicted and field-measured frost depth profiles are shown in Figures 5.7 and 5.8 for the bound asphalt treated section and unbound ODOT 307-IA. The EICM predicted a maximum frost depth of 30 inches. This means that the model had predicted that frost would have occurred in the subbase layer.
However, field measurements in these sections showed that the frost only occurred in the upper layer of the ODOT306-Cement Treated base. On the other hand, field measured data at the 307-IA section showed a frost penetration to 30 inch depth, matching the results obtained from the EICM predictions. Field measurement at the ODOT 308-
Asphalt Treated section did not show any frost penetration at all.
It can be seen that EICM can give a good prediction of the frost depth in the unbound base materials, but somewhat poorer predictions in the bound material.
Modeling of the bound base material should be different from modeling of the unbound materials. Thermal properties, such as thermal conductivity and heat capacity, need to be better quantified for the bound base materials.
149
Figure 5.7 Predicted Frost Depth for 307-IA section
Figure 5.8 Frost Depth for ODOT 306-Cement Treated section.
150
5.4.3 Moisture Data Comparison
Comparisons of the model predicted moisture content and field measured are presented in Figures 5.9 and 5.10 for unbound and bound base material sections, respectively. From these figures, it can be seen that EICM can provide a good prediction of the moisture profile for the base and subbase materials. The seasonal trend in moisture content variation predicted by the model is reasonably consistent with those observed in the measured data. Some sections nevertheless showed discrepancy between the predicted and measured moisture content, especially in the subgrade. This discrepancy may be explained by the fact that the material properties obtained from the lab tests are different from the actual field material properties. One of the main concerns here is the dry density; for example, the lab dry density result for A-6 subgrade was 1.65 g/cm3, which is different from field density shown in Table 5.2. To investigate if improved model predictions could be achieved by using more representative in-situ density data, the actual measured dry density from Equation 5-2 in this chapter was used. Furthermore, subgrade layer was subdivided into many sub-layers, each with representative dry density, porosity and initial water content.
Figure 5.11 shows a comparison between the measured and the predicted gravimetric water content by EICM using the representative in-situ density as input. The results clearly show that using actual field dry density as an input for the EICM model could yield an improved agreement between the predicted and measured moisture content.
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307-IA 307-CE June 04-June05 Oct 03-June05
Moisture Content Moisture Content 0 102030405060 0 1020304050 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in)
60 60
70 70
80 80
EICM3.02 EICM 3.02 90 90 Measured Data (Min) Measured Data (Min) Measured Data (max) Measured Data (max) 100 100
ODOT- 304 Oct 03-June05
Moisture Content 0 0.1 0.2 0.3 0.4 0.5 0.6 0
10
20
30
40
50 Depth (in) 60
70
80
EICM3.02 90 Measured Data (Min) Measured Data (max) 100
Figure 5.9 Moisture Content Profile for unbound Granular Base materials.
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Cement Treated Asphalt Treated Oct 03-June05 Oct 03-June05
Moisture Content Moisture Content 0 1020304050 0 1020304050 0 0
10 10
20 20
30 30
40 40
50 50 Depth (in) Depth (in) 60 60
70 70
80 80
EICM3.02 90 EICM3.02 Measured Data (Min) 90 Measured Data (Min) Measured Data (max) 100 Measured Data (max) 100
Figure 5.10 Moisture Content profile for Bound Granular Base Materials
Table 5.2 Field dry density results for 307-NJ section, driving lane, at Phase II site. (La/L) Profile Laboratory Calibration Material TDR Calculated depth Oven-Dry Coefficients Type Initial P (lb/ft3) (in) W.C % d reading a b 17 307- NJ 4.03 2.58 1.18 6.42 111.7 ODOT 22 5.57 2.6 1.18 6.42 105.5 304 ODOT 28 304 & 7.4 2.94 1.18 6.42 111.1 Rockfill 34 A-6a 13.61 3.04 1.28 5.61 93.0 40 A-6a 15.4 2.95 1.28 5.61 85.5 46 A-6a 13.53 3.27 1.28 5.61 99.8 52 A-6a 14.25 3.49 1.28 5.61 104.2 64 A-6a 17.8 3.55 1.28 5.61 96.7 76 A-6a 17.73 3.42 1.28 5.61 93.6 88 A-6a 16.8 3.19 1.28 5.61 89.2 153
307-NJ Oct 03 04-June05 Moisture Content 0 5 10 15 20 25 30 35 0
10
20
30
40 Depth (in)Depth
50
60
70
EICM Modified Inputs 80 Measured Data (Min) Measured Data (max) EICM 3.02
Figure 5.11 Moisture Content Profile for 307-NJ, using actual dry density.
5.5 Sensitivity Analysis for Temperature Predictions
Several asphalt properties control the heat flow through the pavement system and thereby influence the temperature and moisture regimes. The main inputs for material properties that enter the EICM temperature calculations include: surface shortwave absorptivity, thermal conductivity, and heat or thermal capacity. 154
According to the M-E design guide, at input Level 1, direct measurement of these properties is recommended. ASTM E-1952 and ASTM D-2766 are laboratory procedures for measuring thermal conductivity and heat capacity, respectively. However, although there are procedures in existence to measure shortwave absorptivity, their applicability to paving materials is not well established.
The sensitivity computations for temperature profiles for ODOT 304 conventional fill base section are shown in Figure 5.12. It can be seen that at the same thermal conductivity and heat capacity, an increase in the surface shortwave absorptivity results in an increase in the predicted temperatures. This effect is most pronounced at asphalt surface.
Figure 5.13 shows the effect of the changing thermal conductivity and heat capacity, while keeping the shortwave absorptivity at 0.7. Figure 5.14 shows the effects of the asphalt density on the predicted temperature profile. In these figure it can be seen that the effects of the shortwave absorptivity are more important than the effects of other thermal properties. Asphalt density does not exert any effect on the temperature prediction.
155
Figure 5.12 Effect of the Surface Shortwave Absorptivity at 0.4, 0.81 of thermal conductivity and Heat capacity, respectively.
Figure 5.13 Effect of the Surface Shortwave Absorptivity at 0.7 and different thermal conductivity and Heat capacity on the prediction of temperature profile.
156
Figure 5.14 Effect of the asphalt density on the prediction of temperature profile.
5.6 Summary and Conclusions
The Enhanced Integrated Climatic Model was used to predict temperature, moisture and frost depth data at the ATB-90. Project Site II, in Ashtabula County, Ohio.
Comparisons were made between the predicted and measured moisture contents and temperature along the depth of pavement sections as well as frost depth at different times during the simulation period. The model was shown to be capable of predicting the expected moisture and temperature variations that the pavement was subjected to.
One of the main deficiencies of the EICM was the prediction of pavement surface temperature. In this study, a wide discrepancy of up to 15º F was observed between the predicted and measured temperature at the pavement surface.
The capability of the EICM to predict pavement frost depth was shown to be good for the pavement sections built with unbound permeable base materials. On the other hand, it was difficult to achieve good predictions for pavement sections built with bound 157
base materials. Further development of the model capability to account for different types of treated (bound) granular base materials is recommended.
The capability of the EICM to predict moisture variations was evaluated. A good agreement between predicted and measured moisture content profiles was found, especially for the base and subbase materials. For subgrade materials, a small variation between model predicted results and field measured results was found; however, it was shown that enhancing the ability of the model to predict moisture content within the subgrade can be done by modeling the subgrade layer with multi sub-layers, with each layer properties obtained from field measured properties, such as dry density, initial moisture content and grain size distribution.
The sensitivity analysis presented in this paper shows that the shortwave absorptivity is the most critical thermal property for accurate thermal temperature prediction.
158
CHAPTER VI
PREDICTING MOISTURE-DEPENDENT RESILIENT MODULUS OF COHESIVE
SOILS USING SOIL SUCTION CONCEPT
The resilient modulus of the materials used in various pavement layers has been used extensively as an important material property in structural design of pavement. The state of stress and moisture content of cohesive soils have been observed to exert significant effects on the measured resilient modulus. Since the moisture content in the cohesive subgrade soils underneath the pavement undergo seasonal changes due to infiltration of precipitations and since characterization of moisture and stress dependent resilient modulus of cohesive soils is a demanding and tedious task, there is a practical need for a predictive equation for the resilient modulus as a function of stress states and moisture content.
This chapter presents a new predictive equation for the resilient modulus of cohesive soils using the concept of soil suction. The suitability of using matric suction as a state variable in the model for predicting the resilient modulus is validated. The advantage of the proposed model is further contrasted with the previous Yang, et al (2005) model. The accuracy of the proposed model is validated against experimental data of A-4 and A-6 soils as well as other data available in the literature.
159
6.1 Introduction
Resilient modulus, MR, of soils was introduced as an important material property in the 1986 and 1993 AASHTO Guides for design of pavement structures (AASHTO
1986, 1993). In the new Mechanistic-Empirical design guide (MEPDG 2004), the resilient modulus also plays a major role in representing the properties of the materials in various pavement layers. The resilient modulus of cohesive soils is not a constant stiffness property, but highly dependent upon factors such as the state of stress, soil structures, and water content (George 2004). Due to complexity of conducting resilient modulus testing, there have been numerous efforts to develop predictive equations by incorporating state variables such as confining stress, bulk stress, deviator stress, and soil physical properties. However, among the existing predictive equations, only one model proposed by Yang, et al (2005) attempted to predict variation of resilient modulus with changes in soil’s water content. The use of Yang et al (2005) equation is limited due to the need for experimental data of the soils at different water contents. There is a need to develop a simple and accurate prediction equation of the variation of resilient modulus with the water content of the soils.
The importance of the water content in affecting the resilient modulus of soils has been well documented by the past researches. For example, Drumm, et al. (1997) showed a significant reduction of resilient modulus of A-4, A-6, and A-7 soils as the moisture content was increased above the optimum moisture content. Pezo, et al. (1992),
Mohammad, et al (1996), Wolfe and Butalia (2004) have observed the significant influences exerted by the water content on the measured resilient modulus of the cohesive soils. The moisture content of the subgrade soils underneath the pavement is usually 160
varied over time. According to Uzan (1998), the clayey soils underneath the pavement exhibit an increase in moisture content to about 20 to 30 percent higher than the plastic limit of the soil. This occurs during the first three to five years of pavement service.
Similarly, Elfino and Davidson (1989), Thadkamalla and George (1995), and Uzan (1998) indicated that the moisture content of the subgrade soils would vary with season until reaching an equilibrium moisture content (EMC). In view of the sensitivity of the resilient modulus of cohesive soils to the water content and stress state and the likelihood of the soils’ moisture variation underneath the pavement, it is important to develop a simple and accurate prediction equation for predicting the variation of resilient modulus due to changes in stress and moisture content of cohesive soils.
6.2 Resilient Modulus Models
The concept of resilient modulus has been used to represent the nonlinear stress- strain characteristics of subgrade soils. Several constitutive models for modeling resilient modulus of soils have been proposed in the past. Among them, Seed et al. (1967) proposed a relationship where resilient modulus is a function of bulk stress.
K2 M R = K1 (θ / pa ) (6-1) where
MR = resilient modulus
θ = bulk stress =(σ1+σ2+σ3 ), σ1,σ2,σ3 are three principal stresses.
Moossazadeh and Witczak (1981) proposed a relationships known as the deviator stress model for cohesive soils.
K2 M R = K1 (σ d / pa ) (6-2) 161
where
σd = deviator stress = (σ1 - σ3).
Uzan (1985) present a so-called universal model as follows.
K2 K3 M R = K1Pa (θ / Pa ) (τ oct / pa ) (6-3) where
τ oct = Octahedral shear stress, for triaxial conditionτ oct = 2 / 3 (σ 1 −σ 3 ) .
Pa = atmospheric pressure.
The generalized model adopted by MEPDG is given below.
k2 k3 ⎛ θ ⎞ ⎛τ oct ⎞ M R = k1 pa ⎜ ⎟ ⎜ +1⎟ (6-4) ⎝ pa ⎠ ⎝ pa ⎠
The coefficients k1, k2, and k3 in above equations are regression constants.
Most of the State Highway Agencies in the United States do not routinely measure resilient modulus in the laboratory; however, the resilient modulus used for design was estimated either from experience or from other material properties (George
2004). For example, Von Quintus and Killingsworth (1998), Dai et al.(2002), Santha
(1994) and Mohammad et al. (1999) have developed prediction equations for resilient modulus by relating the regression coefficients of the models to the soil physical properties. Similarly, Carmichael and Stuart (1986), Drumm et al. (1997), Lee, et al.
(1995, 1997), Burczyk, et al. (1994) and Brown and Pappin (1981) have developed prediction equations for the resilient modulus of cohesive soils based on simple laboratory tests results. A review of the above mentioned prediction or correlation models for cohesive soils indicate that none of them explicitly takes into account of the moisture
162
effect. The current MEPDG adopts the following model to predict the change of modulus due to a change in degree of saturation of the soils, is shown in Equation 4.1 and recall here:
M b − a log R = a + (6-5) M Ropt ⎛ − b ⎞ 1+ EXP⎜ln + km.()S − Sopt ⎟ ⎝ a ⎠ where:
M /M Resilient modulus ratio; M is the resilient modulus at a given degree of R Ropt = R
saturation and M is the resilient modulus at a reference condition. Ropt
a = Minimum of log(M /M ). R Ropt
b = Maximum of log(M /M ). R Ropt
km = Regression parameter.
(S-Sopt) = Variation in degree of saturation expressed in decimal.
The values of a, b, and km for coarse-grained and fine-grained materials are
summarized in Table 6.1.
Table 6.1 Values of a, b, and k for coarse-grained and fine-grained materials (MEPDG, m 2004) Coarse- Fine-Grained Parameter Grained Materials Materials a - 0.3123 -0.5934
b 0.3 0.4 k m 6.8157 6.1324
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The MEPDG equation is very general and relied on empirical regression constants. More importantly, the MEPDG equation does not combine the effects of state of stress and water content.
6.3 Previous Models Incorporating Soil Suction
The resilient modulus of cohesive soils is not only sensitive to the state of stress of the soil, but also to the presence of water and the capillary suction. In recent years, interest in determining the soil suction of unsaturated subgrade soils beneath a pavement has increased markedly due to the fact that soil suction dictates the state of stress in unsaturated soils (Fredlund and Rahardjo 1993).
Strong correlation between soil suction and resilient modulus was also observed by other researchers, such as Sauer and Monismith (1968), Khoury et al. (2003), and
Khoury and Zaman (2004). It is worthy of noting that Fredlund, et al. (1977) demonstrated that the resilient modulus is a function of three stress variables: the net confining stress, the deviator stress, and the matric suction.
The only model that explicitly includes the effect of stress and suction is the one developed by Yang et al. (2005) as follows.
k2 M R = k1()σ d + χwψ m (6-6) where
σ d =deviator stress,
χw = effective stress parameter,
ψm = matric suction, k1, k2= regression coefficients 164
One of the shortcomings of Yang et al. (2005) model is that in order to provide a reasonable prediction of resilient modulus, the model parameters K1 and K2 must be calibrated at each water content. As illustrated in Figure 6.1 for A-4 and A-6 soils, that the model predictions based on regression constants obtained for one water content can not be used for predicting the resilient modulus for the same soil at other water contents.
100 90 80 70 R 60 50 40 Predicted M 30 20 A-4 Soil 10 A-6 Soil 0 0 102030405060708090100
Measured MR
Figure 6.1 Comparison between predicted and measured resilient modulus for A-4 and A- 6 soils using Yang et al., (2005) model
6.4 Proposed Model Incorporating Soil Suction Concept
The proposed approach for including the effect of matric suction on the resilient modulus prediction requires the use of the effective stress relationship originally proposed by Bishop (1959), where the net normal stress (σ- ua) and the matric suction (ua
- uw) are two stress state parameters.
σ′ = (σ - ua) + χw(ua - uw) (6-7)
165
where
σ' = effective stress,
σ= total stress,
χw = Bishop’s parameter, ua and uw = pore air and pore water pressures, respectively.
χw has the same numerical limit as the degree of saturation (0 < χw < 1). χw represents the contribution of matric suction to the effective stress and can be considered as the weight of matric suction on effective stress. For saturated soils, χw is equal to 1.
For unsaturated soils, Khalili and Khabbaz (1998) concluded that the relationship between χw and matric suction is linear in a log-log space at the suction level above the air-entry value.
0.55 ⎛ (u − u ) ⎞ ⎜ a w b ⎟ χ w = ⎜ ⎟ (6-8) ⎝ ua − uw ⎠ where (ua − uw )b = air entry value = the matric suction where air starts to enter the largest pores in the soil. (ua − uw ) = ψm = matric suction.
Using effective stresses and assuming the pore air pressure equal to zero (ua=0), the proposed model for predicting the effect of moisture variation on resilient modulus of unsaturated cohesive soils takes the following form:
K2 K3 ⎛θ + χwψ m ⎞ ⎛τ oct ⎞ M R = K1Pa ⎜ ⎟ ⎜ +1⎟ (6-9) ⎝ pa ⎠ ⎝ pa ⎠ where
θ = bulk stress = σ1+σ2+σ3, where σ1 , σ2, σ3 are three principal stresses ,
166
τ oct = Octahedral shear stress, for triaxial conditionτ oct = 2 / 3 (σ 1 −σ 3 )
ψm = Matric suction,
χw = Bishop’s parameter
Pa = Atmospheric pressure
K1, K2, K3 = Regression constants.
6.5 Soil Suction
Soil suction is comprised of two components: matric suction and osmotic suction.
The matric suction represents attraction due to capillary and surface adsorptive forces of unsaturated soils; the osmotic suction represents attraction of water due to the presence of dissolved salts in the pore fluid.
6.5.1 Soil Suction Concept
Soil suction is a negative pressure, opposite to that of atmospheric pressure. In soil mechanics convention, soil suction is expressed as a positive value.
ψ= (ua - uw)+π (6-10)
where ψ is the total soil suction and π is the osmotic suction. The osmotic suction has negligible effect on effective stress; therefore, the matric suction is the major portion of the total suction that affects the effective stress in the soils (Fredlund and Rahardjo,
1993).
Devices and methods for measuring soil suction and soil-water characteristics curves are numerous. The filter paper method, specified in ASTM D 5928-99, is a relatively simple and inexpensive test method that covers the full range of suction.
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According to Fredlund and Rahardjo (1993), the filter paper method is the only method that is capable of measuring the total suction and the matric suction simultaneously. The filter paper method is based on an assumption that a filter paper will reach equilibrium with respect to moisture flow within a soil. If a filter paper is placed in contact with the soil, the matric suction is measured. If the filter paper and soil are not in contact (but still in the same airtight container), the total suction is measured. After achieving equilibrium, the water contents of the soil and filter paper are measured and the suction in the soil is calculated using a relation between filter paper water content and suction.
A typical calibration curve for a filter paper method consists of two parts, as shown in Figure 6.2. The upper segment represents moisture retained as films adsorbed onto particle surfaces, while the lower segment represents moisture retained by capillary or surface tension forces between particles. The breakpoint between these two segments is roughly at the filter paper water content of 45 percent.
6.5.2 Soil Water Characteristic Curve (SWCC)
The SWCC is defined as the variation of water storage capacity within the macro- and micro-pores of a soil, with respect to suction (Fredlund and Xing 1995). This relationship is generally plotted as the water content (gravimetric, volumetric, or degree of saturation) versus the soil suction. Several studies have been conducted to develop empirical equations for representing the SWCC (Zapata 1999). In general, the equation proposed by Fredlund and Xing (1994) has been found to agree with an extended database and was adopted in the MEPDG. A significant amount of effort was expended
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to obtain the fitting parameters of the Fredlund and Xing equation based on the soil index properties (Zapata 1999).
Figure 6.2 Typical Filter Paper calibration curve
The shape of the soil-water characteristic curve is highly dependent on the material type. There is considerable variation in the SWCC due to different measurement methods, different soil sampling techniques, and inherent variability between samples
(Vanapalli et al. 1999). Fine-grained soils (such as clays) generally have higher matric suction than coarse-grained soils, while loose clays can undergo large volumetric changes as a result of changes in suction (Heath et al. 2004).
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6.6 Validation of the Proposed Model
The triaxial tests following AASHTO T307-99 procedure were conducted on the
A-4a and A-6a cohesive soils compacted at optimum water content and 2% above the optimum. Table 6.2 shows a summary of the index and physical properties of the subgrade soils used in this study.
Table 6.2 Summary of the particle-size analysis, Atterberg’s limits, and compaction properties for cohesive soils in this study Atterberg's Optimum % Passing for sieve No. Max Dry Limits Water Density Soil No. No. No. No. No. 3 Content LL PL PI (lb/ft ) Classification 4 10 40 100 200 (%) A4-a 94.7 88 76 66 56 28 20 8 113 14.2
A6-a 94 88 81 75 68 30 18 12 112.7 16.5
The SWCC’s for A-4 and A-6 soils were measured using the filter paper method with Whatman No. 42 filter paper (ash-free quantitative Type II with a diameter of 5.5 cm). The results are shown in Figures 6.3 and 6.4 for A-4 and A-6 soils, respectively. It can be seen that the Fredlund and Xing equation provides good match to the experimental data.
The measured resilient modulus for A-4 and A-6 soils are presented in Figures 6.5 and 6. It is observed that an increase in moisture content and deviator stress decreases the resilient modulus.
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A-4 Subgrade 45 Measured suction 40 35 Fredlund and Xing 1994 30 25 20 15 10 Air Entry Value Volumetric Water Content Water Volumetric 5 0 1 10 100 1000 10000 100000 Matric Suction (KPa)
Figure 6.3 Measured and predicted matric suction for A-4 soil
A-6 Subgrade
40 Measured suction 35 Fredlund and Xing 1994 30 25 20 15 10 Air Entry Value
Volumetric Water Content Volumetric 5 0 1 10 100 1000 10000 100000 1000000 Matric Suction (kPa)
Figure 6.4 Measured and predicted matric suction for A-6 soil
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100 OMC-14 kpa OMC-28 kpa 90 OMC-42 kpa OMC+2%-14 kpa 80 OMC+2%-28 kPa OMC+2%-42 kpa 70
60 Resilient Modulus. MPa 50
40 0 1020304050607080 Deviator Stress, KPa
Figure 6.5 Effect of moisture content and stress state on resilient modulus for A-4 soil
60 OMC_ σ3=42 KPa OMC_ σ3=28 KPa OMC_ σ3=14 KPa 50 OMC+2%_σ3=42 KPa OMC+2%_σ3=28 KPa OMC+2%_σ3=42 KPa 40
Resilient Modulus. MPa 30
20 0 20406080 Deviator Stress, KPa
Figure 6.6 Effect of moisture content and stress state on resilient modulus for A-6 soil
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Predictions by Equation (6-9) are compared with the measured in Figures 6.7 and
6.8 for A-4 and A-6 soils, respectively. The values of χw and ψm were used are equal
(0.26, 400) and (0.38, 200) for A-4 subgrade soil and (0.25, 300) and (0.37, 150) for A-6 soil at optimum water content and 2% above the optimum, respectively.
A-4 Subgrade 100 R2 = 0.95 90 80 70 R 60 R²=0.62 50 40 Predicted M 30 20 WITH SUCTION 10 NO SUCTION 0 0 20406080100
Measured MR
Figure 6.7 Predicted versus measured resilient modulus for A-4 soils
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A-6 Subgrade 60
50 R²=0.29 40 R R²=0.95
30
Predicted M 20
10 WITH SUCTION NO SUCTION 0 0 102030405060
Measured MR
Figure 6.8 Predicted versus measured resilient modulus for A-6 soils
When a total stress approach is used (i.e., soil suction is ignored), where the model parameters were determined using a regression analysis on all data points, the coefficients of correlation between the measured and predicted are 0.62 for A-4 soil and 0.29 for A-6 soil.
When the matric suction is included in the model, with the suction values obtained by the filter paper method and the regression constant determined from data points at optimum moisture content, the coefficient of correlation is 0.94. The regression coefficients K1, K2, and K3 and the coefficient of correlations are summarized in Table 6.3.
Table 6.3 Regression parameters for proposed model for subgrade soil Moisture 2 Soil Content K1 K2 K3 R No Suction 1.243 0.178 -0.644 0.62 A-4 With suction 0.878 0.404 -0.645 0.94 No Suction 0.625 0.146 -0.458 0.29 A-6 With Suction 0.381 0.436 -0.459 0.95
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To further validate the model, data obtained from other sources are used.
Predictions of resilient modulus by Equation (6-9) are compared with the measured resilient modulus in Figure 6.9 for different types of cohesive soils. For data without direct measurements of soil suction, the Fredlund and Xing empirical equation was used to determine the SWCC. Regression analysis was made to obtain the model parameters at optimum water content. The model with regression analysis parameters from optimum water content was then used to predict the resilient modulus values for the same soil compacted at other water contents. It can be seen that the proposed model gives good prediction of the variation of the resilient modulus due to changes of moisture content.
Higher correlation values were obtained for A-7-6 subgrade materials, when compared to those for A-4 and A-6 soils. This is due to the fact that A-7- 6 soils contain more clayey content, thus resulting in higher matric suction. If SWCC were actually measured, the proposed model would give better predictions. Nevertheless, the proposed model still provides an accurate prediction of the resilient modulus; even the suction values used in prediction were estimated using Fredlund and Xing empirical equation.
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Figure 6.9 Comparison between predicted and measured resilient modulus using proposed model for A-4, A-6, and A-7-6 soil (data taken form Mohammed et al, 1996, 2002, Wolfe and Butalia 2004, Khoury and Zaman 2004, Ceratti et al. 2004 and current work) A comparison between the predictions from the proposed model and those predicted by the empirical equation of the MEPDG, Equation (6-5), for A-4 subgrade soil at different degree of saturation is shown in Figure 6.10. The proposed model and the
MEPDG empirical equation predict similar trend of the changes of modulus due to changes in degree of saturation. One advantage of the proposed model over the MEPDG empirical equation is that it can be used to predict resilient modulus at different stress states and moisture contents.
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2.5
2
1.5 , proposed 2
Ropt R = 0.95 /M
R 1
LOG M LOG 0.5
0 0 0.5 1 1.5 2 2.5
LOG MR/MRopt, (MEPDG)
Figure 6.10 Comparison between predictions by the proposed model and AASHTO empirical equation
6.7 Summary of the Proposed Procedure
The procedure of using the proposed model for predicting the variation of resilient modulus of cohesive soils due to moisture and stress changes is summarized below:
1-Obtain the resilient modulus values according to the NCHRP 1-28A and AASHTO T
307 for cohesive soils compacted at an optimum water content.
2-Determine the SWCC of the soil using the filter paper method or any other suitable method.
3- If direct measurement of SWCC is not available, then use Fredlund and Xing (1994) equation (MEPDG, 2004) to estimate SWCC.
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4- Perform a regression analysis to obtain the model parameters of Equation (6-9) for soils compacted at optimum moisture content.
5-Use Equation (6-9), together with the regression coefficients obtained in step 4, to predict the resilient modulus of the soils at other water contents.
6.8 Summary and Conclusions
Based on the concept of effective stress of unsaturated soils, the matric suction of soil was shown to be an important state variable for predicting moisture-dependent resilient modulus of cohesive soils. A prediction model is proposed and shown to be capable of predicting the resilient modulus of cohesive soils over a range of stress states and water content. Higher correlation values were obtained for A-7-6 cohesive soils than
A-4 and A-6 soils using the proposed model. This is because A-7- 6 soils contain more clayey content, resulting in high suction. A good correlation was also found between the proposed model and the empirical model adopted in the new MEPDG to account for the variation of resilient modulus with variation of degree of saturation. The proposed model, however, provides added advantage in that it can be used to predict resilient modulus at different stress states and moisture contents.
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CHAPTER VII
EVALUATION OF MECHANISTIC EMPIRICAL DESIGN APPROACH OVER
PERMEABLE BASE MATERIALS
This chapter presents the MEPDG evaluation of the effect of moisture on performance of asphalt pavements sections built with different types of permeable base materials. The evaluation procedures and the essential representative material properties are described in this chapter. The impact of the most significant parameter in the design procedure on pavement performance and service life prediction such as: thickness design
(asphalt concrete and base thicknesses), environment (moisture content variation), and material properties (base and subgrade resilient modulus) are evaluated. MEPDG software with the April 2007 version (0.99) is used to conduct a sensitivity analysis to study the sensitivity of the output variables due to variations in the key input parameters used in the design process. Analysis of stresses and strains in pavement layers using
MLEA is conducted to confirm the MEPDG outcomes.
7.1 Introduction
The 1993 AASHTO pavement design guide and its earlier versions were developed from the AASHO Road Test experiment data. That test was conducted
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between 1958 and 1960, with limited structural sections at one location, Ottawa, Illinois, and with modest traffic levels compared to those of the present day. As such, designs accomplished with the 1993 AASHTO guide are projected far beyond the inference space of the original data.
The NCHRP project 1-37A was launched to develop the new M-E Design Guide
(2004). The new proposed Mechanistic- empirical Pavement Design Guide (MEPDG) provides significant potential benefits over the 1993 AASHTO guide in achieving cost- effective pavement designs and rehabilitation strategies. Most importantly, its user- oriented computational software implements an integrated analysis approach for predicting pavement condition over time that accounts for the interaction of traffic, climate, and pavement structure. The eventual implementation of mechanistic flexible pavement design procedures requires the determination of changes in material properties for an accurate evaluation of pavement life and proper determination of required layer thicknesses.
Environmental factors have exerted significant influences on pavement performance, particularly in regions such as Ohio, where large seasonal change in environmental parameters has been observed. It is desirable to observe and correlate environmental factors with pavement performance. Moisture is one of the most important environmental factors, as it affects material properties such as stiffness and strength.
Recent pavement design guidelines include a permeable base layer that serves to provide a medium to remove the water that enters the pavement. To effectively drain surface water infiltration, drainage layers must be designed with an optimal combination of thickness and horizontal permeability. 180
One of the greatest differences between MEPDG procedure and the 1993
AASHTO guide is the material properties required. For example, in the 1993 AASHTO guide, there are only a few parameters identified as material properties: the structural layer coefficients, the layer drainage coefficients, and the subgrade resilient modulus
(AASHTO 1993). These parameters are not enough to describe complex material behavior such as stress-dependent stiffness of unbound materials and time- and temperature-dependent response of asphalt mixtures.
The MEPDG procedure requires engineering properties of layer materials for a mechanistic analysis of pavement responses. In the case of flexible pavements, these properties are (1) dynamic modulus for asphalt mixtures, and (2) resilient modulus for unbound materials. These properties are also environment-dependent, and seasonal variations in temperature and moisture affect their values. The Mechanistic Empirical
Design Approach fully considers the change of temperature and moisture profiles in the pavement structure and subgrade over the design life of a pavement, through the use of a climatic modeling software referred to as the Enhanced Integrated Climatic Model
(EICM), (MEPDG, 2004). The EICM predicts variations of temperature and moisture throughout the seasons and within the pavement structure that are used to adjust the material property for that particular environmental condition.
7.2 Characterization of Base and Subgrade Materials
Resilient modulus, MR, of soils was introduced as an important material parameter in the 1986 AASHTO Guide for design of pavement structures (AASHTO
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1986). In the new MEPDG (2004), the resilient modulus also plays a major role in representing the properties of the materials in base, subbase and subgrade materials.
The Design Guide uses a hierarchical approach which allows the designer flexibility in selecting the design inputs based on the importance of the project and available information. Three levels of design are provided as follows.
7.2.1 Resilient Modulus-Level 1 design: Laboratory testing
Level 1 input is the highest quality of data. The input data is obtained from direct testing on the actual material in question. For Level 1 designs, the resilient modulus values of base and subgrade materials, subgrade, and bedrock are determined from laboratory resilient modulus testing. The NCHRP report on the new MEPDG recommends MR to be obtained from the repeated triaxial testing or resilient modulus testing following NCHPR 1-28 A, “Harmonized test methods for laboratory determination of resilient modulus for flexible pavement design” or AASHTO T307,
“Determining the resilient modulus of soil and aggregate materials”.
The generalized model adopted by MEPDG is given below.
k2 k3 ⎛ θ ⎞ ⎛τ oct ⎞ M R = k1 pa ⎜ ⎟ ⎜ +1⎟ (7-1) ⎝ pa ⎠ ⎝ pa ⎠
Where
MR = resilient modulus
θ = bulk stress =(σ1+σ2+σ3 ), σ1,σ2,σ3 are three principal stresses
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τ 2 oct = Octahedral shear stress, for triaxial condition τ = (σ −σ ) . oct 3 1 3
The coefficients k1, k2, and k3 in above equations are regression constants. The nonlinear elastic regression coefficients of the predictive model can be calculated by performing a nonlinear regression analysis for the laboratory MR test data following
AASHTO T 307.
7.2.2 Resilient Modulus-Level 2 design: Correlations with other material properties
Level 2 input is used when direct test results are not available, but results from other testing are, and a relationship exists between them. Laboratory testing is still the preferable source of information for this level of testing. However, if no resilient modulus laboratory test data is available, the value of resilient modulus can be obtained using typical correlations between resilient modulus and physical soil properties (dry unit weight, Atterberg limits, specific gravity) or between resilient modulus and strength properties (i.e., CBR, unconfined compressive strength).
7.2.3 Resilient Modulus-Level 3 design: Typical Values
This level has the lowest level of accuracy and would typically be used for lower volume roadways. At Level 3, not only are direct test results (Level 1) unavailable, but secondary test results (e.g., CBR) (Level 2) are also not available. For input Level 3, the resilient modulus for the optimum moisture content is selected based on the material classification. EICM is used to adjust the representative MR for the seasonal effect of climate.
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7.2.4 Resilient Modulus as Function of Soil Moisture
Moisture content is an important factor affecting resilient behavior of soils.
Generally, for a given soil with the same dry density, the higher the moisture content, the lower the resilient modulus. The MEPDG assumes that the compacted soil are compacted at optimum moisture content (OMC) with maximum dry density and during the design life of the pavement, that they will experience changes only in the moisture content without any change in the dry density. It is also assumed that the initial degree of saturation, Sopt (degree of saturation at OMC), will be in equilibrium depending on drainage properties and environmental conditions (calculated by the EICM) with time, resulting in the final degree of saturation, Sequil.
The M-E Design Guide incorporates a predictive equation within the EICM to predict changes in modulus due to changes in moisture. The current MEPDG adopts the following model to predict the change of modulus due to a change in degree of saturation of the soils:
M b − a log R = a + (7-2) M Ropt ⎛ − b ⎞ 1+ EXP⎜ln + km.()S − Sopt ⎟ ⎝ a ⎠ where: M /M Resilient modulus ratio; M is the resilient modulus at a given degree of R Ropt = R
saturation and M is the resilient modulus at a reference condition. Ropt
a = Minimum of log(M /M ). R Ropt
b = Maximum of log(M /M ). R Ropt
km = Regression parameter.
(S-Sopt) = Variation in degree of saturation expressed in decimal. 184
Figure 7.1 presents the correction factor for the moisture condition for the various degrees of saturation. One can observe that the fine-grained soils are more influenced by the moisture content than the coarse-grained soils. Generally, the degree of saturation of subgrades increases with time, the resilient modulus will decrease over the design period due to the increase in moisture content and reach the minimum resilient modulus.
3
2.5
2
subgrade 1.5 base
Correection Factor 1
0.5
0 -2.5 -1.5 -0.5 0.5 1.5 2.5 (S-Sopt)
Figure 7.1 Correction factor as a function of the degree of saturation.
The MEPDG permits accounting for seasonal variation in properties of unbound materials by adjustment of the resilient moduli for each design period
(month). The user has two options:
• Provide the resilient modulus for each design period or
• Provide resilient modulus for the optimum moisture content.
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If the second option is selected, the Enhanced Integrated Climatic Model incorporated into the MEPDG software predicts seasonal variation in the moisture content of the unbound layers.
7.3 M-E Design Guide Performance Models
The MEPDG (2004) considers three primary distress and other factors to predict the smoothness of flexible pavements at any given time:
• Fatigue cracking
• Thermal cracking
• Permanent deformation (rutting)
The main focus in this chapter is on permanent deformation (rutting). Permanent deformation or rutting occurs in the wheel paths in the form of longitudinal depressions and develops as the number of load repetitions accumulates. Rutting is estimated for the
AC and granular materials for each subseason at the mid-depth of each sublayer in the pavement system. The permanent deformation for each sublayer for each subseason is added to estimate the total permanent deformation. The MEPDG procedure recommends an acceptable total rutting limit of 0.75 inches after adjustment for reliability.
7.4 Resilient modulus test results
The resilient modulus test was conducted by following the AASHTO test protocol. Table 7.1 presents resilient modulus for all specimens at different stress and test conditions.
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Table 7.1 Resilient modulus test results Stress Unbound Materials (MPa) Bound Materials (MPa) 304- 304- 307- cement σ3, σd, 304 – Media Coars 307- 307- CE treated o o o Kpa Kpa Fine n e No.57 NJ IA 32 F 77 F 104 F 3 3 28.4 23.8 32.3 31.1 31.0 27.8 32.4 174.0 100.2 38.9 223.8 3 6 31.2 24.0 34.5 31.0 31.8 29.6 34.3 181.0 103.0 42.3 227.4 3 9 33.1 25.7 39.1 31.5 34.1 33.0 37.8 189.1 105.5 45.4 237.1 5 5 36.5 28.0 42.2 34.9 39.9 36.1 41.3 191.0 117.1 50.8 243.5 5 10 40.0 31.2 46.6 37.0 43.0 40.8 46.4 209.9 113.7 52.5 258.9 5 15 40.0 32.6 53.4 37.6 45.3 43.9 49.6 221.4 114.5 55.3 273.0 10 10 50.9 42.7 64.8 52.9 61.1 56.2 63.2 234.9 131.3 68.6 296.2 10 20 54.2 47.4 72.9 55.2 65.7 62.7 69.9 246.8 130.0 70.1 314.4 10 30 54.5 50.8 77.7 58.2 67.8 66.2 72.0 254.2 131.7 74.5 320.2 15 10 55.5 51.1 76.1 65.8 72.4 66.8 74.7 252.2 143.7 82.3 306.9 15 15 58.4 53.7 80.6 68.4 73.4 69.4 76.8 264.4 142.2 83.6 316.6 15 30 66.8 63.1 93.3 72.2 80.4 78.1 86.6 274.8 142.9 87.5 341.4 20 15 70.7 63.5 95.6 79.1 86.0 81.5 90.3 279.1 153.2 94.1 329.8 20 20 77.5 70.2 102.0 81.6 89.0 84.9 93.3 288.2 152.6 94.5 340.7 20 40 82.7 80.9 112.9 85.6 97.4 95.0 104.5 300.8 154.2 100.0 366.1
7.5 Procedure Implementation
In the structural analysis associated with stress and strain developed in the layers subjected to traffic loadings, the existing AASHTO Design Guide is based on MLEA, while the new MEPDG offers two types of analyses, the MLEA and the 2-D Finite
Element Analysis (FEA). The MLEA assumes a constant representative resilient modulus
(MR ) for each layer, whereas the FEA employs a stress-dependent resilient modulus for
the Level 1 design. The MEPDG methodology uses the Multi-Layer Linear Elastic
Theory (MLET) to predict mechanistic responses in the pavement structure. When level 1
nonlinear stiffness inputs for unbound material are selected, MLET is not appropriate and
a nonlinear Finite Element Method (FEM) is used instead. According to the NCHRP
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report on this new M-E Design Guide, the FEA needs further calibration before it can be implemented (MEPDG).
Level 2 and 3 was used throughout this study because at present Level 1 inputs utilize the stress dependent FEM. Unfortunately, the FEM program has not been calibrated with distress. The final version of the MEPDG software was calibrated using level 3
(MEPDG 2004).
The pavement structure for ATB-90 project conditions was the reference case for this parametric study. General properties, such as reference temperature and Poisson's ratio, were not included in this study and thus the values were kept equal to the default values provided by the MEPDG software. Table 2 summarizes the traffic volume data for
ATB-90 project. The vehicle class distributions assumed for the traffic were based on the
NCHRP 1-37A default vehicle distributions.
Table 7.2 traffic volume data AADT 21000 Trucks % 0.45 AADTT 9400 Trucks design dir 0.50 Trucks design lane 90 %
Vehicle operational 70 mph speed The environmental conditions are simulated by EICM. This study used data from weather station at ATB-90. Groundwater table depth in this comparative study was kept constant at 20 ft below the pavement surface for all analyses.
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Level 3 material property inputs for asphalt concrete materials were used in this study. A set of typical material properties was selected for the asphalt concrete. The same asphalt concrete properties were used with all sections.
The MEPDG procedure requires the base and subgrade resilient modulus at optimum moisture and density. The material properties used for the MEPDG analyses in this study are summarized in Table 7.3 through Table 7.5.
Table 7.3 Asphalt concrete properties. General properties Reference temperature (°F) 70 Poisson's ratio 0.35 Volumetrics Effective binder content (%) 9 Air voids (%) 6.2 Total unit weight (pcf) 148 Gradation Cumulative % Retained 3/4 inch sieve 23 Cumulative % Retained 3/8 inch sieve 46 Cumulative % Retained #4 sieve 60 % Passing #200 sieve 2.9 Thermal properties Thermal conductivity asphalt (BTU/hr-ft-F°) 0.67 Heat capacity asphalt (BTU/lb-F°) 0.23 Binder grade PG 64-22
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Table 7.4 Granular aggregate base properties Strength properties No.57 304- 304- 304- 307-ce 307-ia 307-nj coarse med fine Poisson's ratio 0.35 0.35 0.35 0.35 0.35 0.35 0.35 Coeff. of lateral pressure, Ko 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Plasticity Index, PI 0 0 0 0 0 0 0 Passing #200 sieve (%) 0.5 0 7.5 15 1.0 3 1.0 Passing #4 sieve (%) 1.5 7.5 18.8 60.0 27.5 40.3 2.5 D60 (mm) 15.6 21.8 10.9 4.75 13.4 10.27 8.13 Maximum dry unit weight (pcf) 107 130 132 136 125 130 127 Specific gravity of solids, Gs 2.6 2.6 2.6 2.6 2.6 2.6 2.6 Saturated hydraulic conductivity 1130 230 59 8 150 95 160 (ft/hr) Optimum gravimetric water 2.0 3 6 7 2.0 2.5 2.0 Calculated degree of saturation 12.2 56.7 68.1 94.3 17.5 26.2 18.7
Table 7.5 Subgrade properties AASHTO classification of soil A-6 Poisson's ratio 0.35 Coeff. of lateral pressure, KO 0.5 Resilient modulus (psi) 7,500 Plasticity Index, PI 12.3 Passing #200 sieve (%) 68.8 Passing #4 sieve (%) 94.1 D60 (mm) 0.032 Maximum dry unit weight 113 Specific gravity of solids, Gs 2.70 Saturated hydraulic 6.5x10-6 conductivity (ft/hr) Optimum gravimetric water content (%) 16.2
Calculated degree of saturation (%) 89.1
To evaluate the effect of moisture content on pavement performance, resilient modulus at different degree of saturation were calculated using Equation 7-2 for unbound granular and subgrade materials.
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7.5 Results and Analysis
The objectives of the MEPDG analyses were to evaluate the effect of environmental conditions on the predictions of individual structural distresses for asphalt pavement over different types of base Materials.
Material characterization is one of the most significant input changes in the M-E design procedure. The pavement structural response models require more mechanical properties, as well as additional thermo-hydraulic properties for the climate models. This chapter describes the influence of different permeable base material properties on performance prediction in the MEPDG procedure.
Resilient modulus of base material is intuitively expected to affect the overall pavement performance. Stiffer base layers reduce the vertical compressive strains within the base layer and subgrade, consequently reducing permanent deformation. Figure 7.1 shows these trends for the MEPDG predictions.
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0.9
0.8 design 0.7 li i
0.6
0.5 AC rut
0.4 Base rut rutting (in) 0.3 Subgrade rut total rut 0.2
0.1
0 no57 307-nj 307-ia 307-ce cement 304-fine 304-med asphalt32 asphalt77 asphalt 104 asphalt 304-coaTse material type
Figure 7.2 Effect of different permeable base materials
Although each base material has different resilient modulus, the individual distress predictions from the MEPDG methodology showed much less variable behavior for rutting performance. Figure 7.2 shows that the MEPDG performance predictions for each material were about the same, considering the same traffic level and different resilient modulus.
Figure 7.2 summarizes permanent deformation in each layer. It is interesting to note the negligible reduction in AC rutting with variations in base layer resilient modulus.
Due to the large EAC/MRbase ratio, the influence of base layer modulus on the vertical compressive strains in the AC layer are only significant near its interface with the base layer.
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Moisture is an important parameter affecting pavement performance. Figure 7.3 shows
MEPDG predicted performance for unbound base materials subjected to different moisture level. The resilient modulus was varied according to Equation 1 for different degree of saturation. It can be seen that increasing moisture content will decrease the resilient modulus for unbound base materials. Results show that increase moisture content in base layer will not result in significant effect on pavement performance as expressed by permanent deformation.
40 60 80 100 120 140 60000 0.7
50000 0.6 AC
0.5 40000 Base
0.4 subgrade 30000
total 0.3 Rut Depth (in) Depth Rut rutting 20000 Resilient 0.2 resilient modulus (psi) modulus
10000 0.1
0 0 40 50 60 70 80 90 100 110 120 130 140 S/Sopt
Figure 7.3 Effect of different degree of saturation Figure 7.4 summarizes the vertical compressive strain profiles for different base resilient moduli for the control section (ODOT 304) computed using MLEA. It can be seen that the AC elastic deformations (vertical strains) are little affected by variations in base MR, and thus the base MR has little influence on rutting within the AC layer.
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Vertical compressive strain 0.E+00 1.E-05 2.E-05 3.E-05 4.E-05 5.E-05 0
5 55 ksi 50 ksi 41 ksi 30 ksi 10 22 ksi AC layer 17 ksi
15 base layer
Depth from the surface (in) 20 subbase layer
25 Rockfill layer
30
Figure 7.4 Multi-layer linear elastic computation of vertical compressive strains versus base MR
Subgrade resilient modulus is also an important parameter affecting pavement performance. Since all sections have the same subgrade materials, the analysis conducted here is to show the effect of moisture content variation in subgrade. Figure 7.5 shows
MEPDG predicted performance for subgrade materials subjected to different moisture level. The resilient modulus was varied according to Equation 7-2 for different degree of saturation. It can be seen that weaker subgrades, represented by low MR values due to high
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moisture content, are associated with poorer performance; rutting decrease with increasing subgrade stiffness, which agrees with expectations.
It is very difficult to study permanent deformation where the same pavement structure is evaluated over different subgrade types and stiffness and environmental conditions. The results in Figure 7.5 show the sensitivity of permanent deformation to soil saturation using MEPDG. Nevertheless, the results show that the MEPDG procedure is capable of capturing some influence of different subgrade types beneath the pavement section.
degree of saturation 40 50 60 70 80 90 100 110 ac 0.45 16000
0.4 14000
0.35 12000 base 0.3 10000 0.25 8000 0.2 subgrade
rut depth (in) 6000
0.15 resilient modulus 4000 0.1
2000 variation 0.05 of reilient modulus 0 0 with 40 50 60 70 80 90 100 105 110 satuartion degree of saturation
Figure 7.5 Sensitivity to subgrade resilient modulus
The parametric study on asphalt and base layers thickness was conducted.
Different permeable base thickness for ODOT 304, ODOT 307, cement treated and
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asphalt treated materials under two different AC thickness were studied, as shown in
Figure 7.6.
1.4
1.2
1 10in-cement 10in-304 10in-307 0.8 10in-asphalt design limit cement 0.6
rut depth (in) 304 307 0.4 asphalt
0.2
0 24681012
Figure 7.6 Sensitivity of MEPDG to base thickness
The results in Figure 7.6 indicate that base layer thickness has little influence on rut performance in the MEPDG methodology. Difference in predicted rut depth was negligible with increased base thickness. It can be seen that for 10 in asphalt layer thickness, the base layer thickness has less effect than at 15 in AC thickness. This result is significantly different from the prediction trend in the 1993 AASHTO guide, in which the base layer thickness can exert considerable influence on the structural number (SN). It is expected that increasing the base layer thickness would increase the overall pavement strength and consequently improve performance. However it can be shown that the
MEPDG results are a direct consequence of the multilayer linear elastic theory used for
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predicting stresses and strains within the pavement structure. Figure 7.7 shows vertical compressive strain versus total pavement depth for different base thickness. Only the calculation results of one set of elastic moduli are shown; however, similar trend can be found for other moduli combinations corresponding to different materials.
Vertical compressive strain 0.E+00 1.E-05 2.E-05 3.E-05 4.E-05 0
5
4 in 10 5 in 6 in AC 7 in layer 8 in 10 in 15
base layer
20 Depth from the surface (in) surface the from Depth subbase layer
25
Rockfill layer
30
Figure 7.7 MLET calculated vertical compressive strain versus pavement depth along the total thickness of the pavement
Figure 7.7 shows that the vertical compressive strain in the AC layer is insensitive to variations in base thickness. Figure 7.6 demonstrates that the MEPDG rutting predictions are consistent with the strain trends in Figure 7.7. The compressive strains in the AC layer are essentially constant, and therefore the predicted rutting in the AC layer is also unchanged. 197
The same type of analysis was done by varying the asphalt concrete layer thickness. Figure 7.8 summarizes the results for rutting performance predicted by the
MEPDG procedure.
1.6 rutting AC 1.4 base subgrade 1.2
1
0.8 Rut Depth 0.6
0.4
0.2
0 6 8 10 12 14 15 18 20 AC THICKNESS
Figure 7.8 Sensitivity of MEPDG to AC thickness.
The results shown in Figure 7.8 are in agreement with MLET analysis. Increasing the AC thickness reduces the compressive strains at the bottom of the AC layer and consequently mitigates rutting. The reduction in computed vertical compressive strains is much more pronounced in the case of AC thickness variation than in the case of base thickness variation. Increasing the thickness of the much stiffer asphalt layer reduces the vertical compressive strain in all layers underneath it, as oppose to what was observed in the base thickness scenario shown in Figure 7.6, Figure 7.8 shows that rutting is reduced in all layers when AC thickness increases.
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The thickness analysis showed that MEPDG procedure emphasizes the structural contribution of the asphalt layer, a direct consequence of the multilayer linear elastic theory analysis. Das and Pandey (1999) found similar results. Using a mechanistic- empirical design method for bituminous roads, they showed that large granular base layer thickness did not allow for much reduction in the asphalt layer thickness to meet the same performance criterion. Their results are in general agreement with MEPDG predictions summarized in this study.
7.6 Pavement Service Life
The objective of this analysis is to show the results presented in previous sections in terms of the predicted service life at specified design criteria rather than in terms of absolute distress magnitude. For permanent deformation, total rut depths of 0.75 inches were used to calculate service life for some of the parametric studies reported in previous sections.
Figure 7.9 shows the predicted service life versus design criterion for different asphalt thicknesses. The results for permanent deformation demonstrate that service life is more sensitive in absolute terms-i.e., number of years of service life-at higher design criterion values. This figure shows that the increase in AC thickness will reduce compressive strain in all pavement layer and increase service life.
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30 rutting
25
20
15 Design Life 10
5
0 6 8 10 12 14 15 18 20 AC THICKNESS Figure 7.9 Service life sensitivity to design criterion for different AC thickness
Plots of service life versus base and subgrade resilient moduli are shown respectively in Figure 7.10 and Figure 7.11. The plots emphasize the conclusions established in earlier sections. The base resilient modulus has little impact on service life for permanent deformation. On the contrary, subgrade resilient modulus has more influence on rutting service life, as would be expected
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30
25
20
15 Design Life, yr 10
5
0 40 50 60 70 80 90 100 110 120 130 140 Degree of Saturation
Figure 7.10 Service life sensitivity to design criterion for different base resilient modulus
40
35
30
25
20
prediction life, yr 15
10
5
0 80 90 100 105 110 saturation degree
Figure 7.11 Service life sensitivity to design criterion for different subgrade resilient modulus.
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7.7 Summary and Conclusions
This chapter contains a simple application of the new mechanistic-empirical pavement design guide to evaluate different permeable base materials pavement sections at ATB-90 project.
The parametric study of the mechanistic-empirical MEPDG methodology provided useful and relevant insights into performance prediction sensitivity to input parameters.
ATB-90 site project conditions were the reference case for the analysis study.
The most common input variables used to characterize open graded base materials (base and subgrade resilient modulus) were investigated using different saturation level.
Results of the impact of open graded base materials on pavement performance show unexpected behavior. It was found that variations in permeable base stiffness had little influence on permanent deformation. This may be a direct result from MLEA. In general, when the base layer thickness is increased it is expected that the overall strength of the pavement should increase and performance should improve. The fact that this parametric study showed a somewhat different trend with increasing base layer thickness may be a due to the simplifications implicit in multi-layer linear elastic modeling of pavement materials. Due to the much higher asphalt concrete stiffness, varying the base thickness did not affect the vertical compressive strain in the asphalt concrete layer.
Subgrade resilient modulus had more impact on permanent deformation than the base resilient modulus. Rutting variations with subgrade stiffness are expected; soft subgrades are more likely to experience higher rutting levels than stiff subgrades.
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Variations of asphalt concrete thickness, on the other hand, were found to have a much more significant impact on performance prediction, which agrees with field expectations. These results suggest that, in the MEPDG methodology, the design process is dominated by the asphalt concrete layer.
The sensitivity of service life to the specified design criteria was also investigated.
It was found that the MEPDG procedure can be used as an effective tool to evaluate the impact of different design criteria on performance and service life of pavement structures.
This type of evaluation is useful to agencies when defining the design criteria for different road classes and environmental conditions. However, given the sensitivity of performance predictions to the unbound materials, level 1 characterization of these materials should be a high implementation priority. Continuing research is needed to better model the behavior of unbound materials in the pavement structure.
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CHAPTER VIII
FINITE ELEMENT MODELING OF FIELD PERFORMANCE OF DIFFERENT
PERMEABLE BASE UNDER ASPHALT PAVEMENT
This chapter presents a calibrated FEM model for simulations of water flow in asphalt pavements with different types of permeable bas materials. The calibration procedures and appropriate initial boundary conditions as well as essential representative material properties are described. In this chapter, the unsaturated water flow in pavement was modeled by a two dimensional finite element program (SEEP/W). FHWA computer program (DRIP) is used to conduct the saturated water flow analysis. The effects of unsaturated conditions on pavement drainage due to different pavement materials characteristics and pavement configuration (geometry, layers, drainage systems) are evaluated. FEM analysis is performed to evaluate the drainage efficiency of the bound and unbound permeable base materials in terms of time required for reaching 50% saturation. The required hydraulic conductivity of the permeable base materials is re- evaluated on the basis of ATB-90 pavement configurations and FEM modeling results.
Parametric study is conducted to study the drainage performance of the permeable base layers due to different parameters such as permeable base hydraulic conductivity, thickness of permeable base layer, subbase hydraulic conductivities, type and location of underdrain pipes, and shoulder slope. 204
8.1 Background
Providing adequate drainage to a pavement system has been considered as an important design consideration to ensure satisfactory performance of the pavement, particularly from the perspectives of life cycle cost and serviceability. Most water in pavements is due to rainfall infiltration into unsaturated pavement layers, through joints, cracks, shoulder edges, and various other defects, especially in older deteriorated pavements (MEPDG, 2004). Water also may seep upward from a high groundwater table due to capillary suction or vapor movements, or it may flow laterally from the pavement edges and side ditches.
Recent pavement design guidelines include a permeable base layer that serves to provide a medium to remove the water that enters the pavement. Higher hydraulic conductivity of base materials compared to subgrade materials allows water to flow by gravity to a collection system (Apul et al., 2002).
Permeable bases are used in both PCC and asphalt concrete pavement. Depending on structural requirements, the permeable base could be bound (e.g., asphalt-treated or cement-treated), or unbound. The primary function of the permeable layer is to collect water infiltrating into the pavement and to move it to the edgedrains within an acceptable time frame. The construction and performance of permeable bases depend on numerous factors, such as the type of material (bound vs. unbound), aggregate gradation, pavement cross slope, shoulder material, and edge drain design, among others. A properly designed and constructed permeable base layer may function as a conventional dense graded base, supporting the pavement by distributing the loads (Apul et al., 2002). Therefore, to
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reduce the time to drain cost-effectively, M-E design guide recommends a minimum laboratory permeability value of 1000 ft/day for permeable bases.
8.2 Hydraulic Design of Sub-Surface Drainage
The permeable base design is most often based on the “time to drain” method, which specifies a time to drain to an allowable saturation. According to M-E design guide
(2004), classification of “excellent” results if the time-to-drain to 50% saturation is less than 2 hours, “good” for less than one day, and so on. Barksdale and Hicks (1977) suggested a time of 2-6 hours for removing 50 percent of the permeable water from airport pavements. Darter and Carpenter (1987) proposed a time of five hours as acceptable to reach an 85 percent saturation level. Nevertheless, McEnroe (1994) related drainage to hydraulic conductivity of materials and noted that for the granular materials a hydraulic conductivity value of 0.074 cm/s (2310 ft/day) was required to achieve 50 percent drainage.
The time-to drain approach is based on the following assumptions:
• Water infiltrates the pavement until the permeable base is saturated.
• Excess runoff will not enter the pavement section after it is saturated.
• After the rainfall event ceases, water is drained to the side ditches or storm
drains through edgedrains or by daylighting.
The FHWA microcomputer program (DRIP), available as part of the software accompanying the M-E guide, can be used to design subsurface drainage for highway pavements. Among the drainage design elements, DRIP allows for the calculation of the time to drain in the drainage layer of a pavement system. This program is based on
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simple analytical prediction methods, which assume that pavement systems are only exposed to saturated conditions. In the time to drain method, DRIP assumes that the drainage layer is saturated at the time to drain and that there is no additional inflow to this layer once the rainfall has ceased. Thus, the hydraulic conductivity is considered as a constant value.
Most of the current drainage criteria have been developed on the basis of experimental field results and theoretical analyses of infiltration under saturated conditions (Ariza and Birgisson, 2001). Conventional drainage systems are only designed to intercept saturated flow driven by gravity. Unsaturated flow can be distinctly different from saturated flow. Unsaturated flow is driven in part by suction gradients, which can result in upward or lateral flow in some situations. Furthermore, when a soil becomes unsaturated, the hydraulic conductivity becomes a function of the negative pore water pressure in the soil. Since pore water pressure is the primary unknown and needs to be determined, therefore iterative numerical techniques are required to match the computed pore water pressure and the material property. This makes the solution process highly non-linear.
Modeling flow of water through soil with a numerical solution can be very complex, since the natural soil deposits are generally highly heterogeneous and nonisotropic. These complexities make it necessary to use some form of numerical analysis to analyze seepage problems. A common approach is to use finite element methods.
FEM simulations can incorporate the principles of unsaturated as well as saturated processes, and can include properties specific to the layers that comprise a pavement
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section. Material properties and pavement layer configurations can be varied in FEM simulation, thus permitting relative rapid parametric studies to be conducted without the need for expensive and time-consuming field experimentation.
Hassan and White (2001) conducted a comprehensive study of pavement subdrainage systems, in which pavement subdrainage system outflow were measured for several rainfall events. A finite element model was developed and calibrated using various test data. Hassan and White (2001) concluded that the efficiency definition as the ratio of outflow to volume of rainfall is an incorrect definition. The drainage system efficiency is better defined as the time to drain.
8.3 Finite Element (FE) Modeling
A FE program for simulating pavement drainage should be capable of handling transient, two-dimensional, saturated/unsaturated flow. In addition, the FE program should provide relatively sophisticated models for near-surface processes. Climatic data should be easily input into the FE program. The ability to incorporate a number of different functions for describing the moisture characteristic curve (moisture content vs. suction) and unsaturated hydraulic conductivity functions (hydraulic conductivity vs. suction) is also important.
In this chapter, the SEEP/W program (SEEP /W, 2004) is used to simulate pavement drinage. SEEP/W is a 2-D finite element software that can be used to model the moisture movement and pore-water pressure distribution within porous materials such as soil and rock. It can model both saturated and unsaturated flow. SEEP/W includes three
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executable programs: DEFINE, for defining the model, SOLVE for solving the problem, and CONTOUR for presenting the results in a graphical form.
SEEP/W assumes that flow in unsaturated soil above the water table follows Darcy
's law in a similar manner to flow in saturated soil. The flow is proportional to the hydraulic gradient and the hydraulic conductivity. The major difference between saturated and unsaturated flow in SEEP/W is that, in a saturated soil, the hydraulic conductivity is insensitive to the pore-water pressure; whereas, in an unsaturated soil, the hydraulic conductivity varies greatly with changes in pore-water pressure.
The hydraulic conductivity of the materials was measured by a constant head rigid wall permeameter. The measured hydraulic conductivities are tabulated in Table 8.1. The hydraulic conductivity of subgrade soils was measured by the flexible wall constant head method and the value is indicated in Table 8.1. The measured soil-water characteristic curves (SWCC) for these materials are shown in Figure 8.1.
The unsaturated flow characteristics was provided (hydraulic conductivity vs. suction). Green and Corey (1971) method was used for predicting unsaturated hydraulic conductivity from SWCC.
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Table 8.1 Hydraulic Conductivity of The Materials Tested
TYPE OF Permeability MATERIAL ft/day (cm/sec) ODOT 304 -Fine 206 (0.073) ODOT 304 -Median 1417 (0.5) ODOT 304 - Coarse 5443 (1.92) ODOT 307 –NJ 3830 (1.349) ODOT 307 – IA 2280 (0.803) ODOT 307– CE 3705 (1.307) AASHTO No. 57 26563 (9.37) Cement Treated 25345 (8.94) Asphalt Treated 25061( 8.84 )
A-6a Subgrade 1.5 x10-4 (5.3x10-8)
24
21 ODOT 304 No. 57 18 307-IA 307-NJ 15 307-CE 12
9
6
3 Volumetric water content in percent in content water Volumetric 0 110100 Matric Suction (KPa)
Figure 8.1 Soil water characteristic curves for different permeable base materials
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The HMA layer was constructed in two lifts of asphalt concrete (ODOT 302) and two lifts of fine surface course layers. The total thickness of the HMA was 15.25 inches at site II.
The subdrainage analyses were conducted using the SEEP/W computer program.
Since the longitudinal slope at ATB-90 was 1.48%; therefore, the pavement was analyzed as a two-dimensional problem, that is, transverse to the centerline. The model consists of a
12 feet pavement lane with 10 and 4 feet of shoulder for driving and passing lanes, respectively. With no water table noticed at 15 ft below the pavement at the ATB-90 site, the subgrade soil was extended to that depth in the model. Figure 8.2 shows the mesh representation of pavement geometry and layer configuration for the test sections at ATB-
90 project. The base layer was represented with 1063 quadrilateral finite elements.
Subgrade was represented by two types of meshes. For better representation of the TDR location, a fine mesh of quadrilateral elements under the pavement layers was used. For subgrade outside the instrumented area, a coarser mesh of triangular elements was used.
A total of 7078 quadrilateral and triangular elements were used altogether. Subgrade was extended laterally 10 feet beyond the area covered by the asphalt and base layer, on each side to represent real conditions more accurately.
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Figure 8.2 Pavement geometry and layer configuration for ATB-90
The highway design at ATB-90 includes two perforated underdrain pipes (4 and 6 inches in diameter) to remove infiltrated water through the permeable base and subbase layers. In numerical simulation, constant pressure head was applied as a boundary condition around the pipes to represent zero water content.
8.3.1 Boundary Conditions
The newly placed hot mix asphalt (HMA) layer was considered as an impervious material; therefore its characteristics were not required as an input in the FEM. Null element (i.e., elements with uncharacterized material with un-assigned conductivity
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function) was used to represent this layer; however, asphalt layer geometry was included in the mesh representation. At the bottom and lateral sides of the subgrade layer, an infinite element was assigned.
8.3.2 Analysis Procedure
To adequately model the non-steady unsaturated flow of water through pavement, a transient analysis was performed, in which the pavement system at initial equilibrium was subjected to a transient condition (precipitation with time), resulting in time- dependent changes in the volumetric moisture content throughout the pavement system.
However, before starting the transient analysis, a steady-state analysis was performed to obtain the initial conditions.
Initial suction values were assigned for each layer so that the initial volumetric water content in the simulation match the measured water content at the beginning of the simulation period. After that, transient analysis was performed, in which actual rain events were infiltrated into the pavement in a time-dependent fashion and the resulting time-dependent changes in heads and volumetric water contents in pavement were computed by FEM model. The precipitation events input into the numerical model corresponds to the actual measurements gathered from October 2003 to July 2006 shown in Figure 8.3. Precipitation was converted into unit flux in the unit of inches per day, per unit area. The converted unit flux was applied as a time-dependent flux boundary condition on the shoulder and extended subgrade to simulate water infiltration due to precipitation.
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50 45 40 35 30 25 20 15 Precipitation, (mm) Precipitation, 10 5 0 9/21/03 12/21/03 3/21/04 6/21/04 9/21/04 12/21/04 3/21/05 6/21/05 Date FallWinterSpringSummer Fall Winter Spring
Figure 8.3 Daily Precipitations at the site
8.3.4 Model Calibration Using Field Data
Materials characterization and boundary condition adjustment in FEM simulation need to be made to match the measured field data. It should be noted that the soil water characteristic curve tend to be sensitive to density and gradation of the test materials
(Ariza and Birgisson 2004). The density of the soil surrounding the TDR probes in the field is likely different from that in the laboratory. As a result, the soil water characteristic curve measured in the lab may not represent the field condition. It is essential to calibrate the measured soil water characteristic curves to fit the measured
TDR data from the field.
To calculate the actual density around the TDR, the equation developed by Al-
Akhras (2004) can be used.
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ρw k a ρd = (8-1) θg b + a
3 where ρd is the dry soil density (g/cm ), and
θg is the gravimetric water content,
Ka is the apparent dielectric constant of the surrounding materials (unitless).
b and a are soil-dependent calibration constants,
Equation (8-) includes two soil-dependent parameters (a and b), which can be
determined from the laboratory calibration for each material. The value of Ka was measured from the TDR probes installed at the site at the time when the soil was
backfilled into the installation hole. The value of θg is determined in the laboratory using the oven-dry method. In this way, initial in-situ dry density of the soils surrounding the
TDR probe can be accurately determined for each TDR probe embedded at the project site.
The first calibration was to use Fredlund and Xing (1994) empirical SWCC equation to generate the SWCC based on the field dry density calculated according to the above procedure. SEEP/W was used to generate the appropriate hydraulic conductivity curves (Fredlund, and Xing, 1994).
The second calibration concerns with the air entry values. Air Entry Value of base material represents the transition suction value between unsaturated and saturated conditions. The higher the air entry value, the longer the material can retain water. The air entry values of the soil water characteristic curves for base materials were adjusted to better represent the likely field conditions. As shown in Figure 8.1, there is no clear air entry value for the granular materials that can be adopted for these materials. Air entry 215
value obtained from the empirical SWCC equation proposed by Fredlund and Xing
(1994) was used. Subsequently, SEEP/W was used to obtain the corresponding hydraulic conductivity curve.
The third calibration was concerns with the amount of precipitation that infiltrates into the pavement subsurface. In fact, not all the water coming from precipitation is going to infiltrate into the soil. However, since the project is new project, visual inspection of the highway asphalt surface does not reveal any surface crack. Hence, an adjustment is needed for the precipitation function to represent the percent of infiltration in the FEM simulation.
To better understand infiltration of water due to precipitation, construction timeline shown in Figure 8.4 at the monitoring sites was examined carefully. The water content monitoring at this site started on the 4th of September 2003 in the Asphalt treated and Cement Treated sections. Monitoring of the 307-IA, 307-NJ and 307-CE sections started on the second week of October 2003. Finally, monitoring of ODOT 304 section started on the 24th of December 2003. Initial sharp jump in the water content at the base, subbase and most of the subgrade soils shown in Figures 8.5 and 8.6, was due to open exposure (no surface protection) to precipitation (Liang 2007).
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Construction Activities Paving Paving Aspahlt Cement Paving 1st lift of Asphalt Layer only over Passing Lane at 307-NJ Treated Treated Base Paving 1st lift of Asphalt Layer over all other Passing Lanes and at Driving Lane in all Sections
Completion of the 1st lift of Asphalt surface Course Layer Date
12-Aug 1-Sep 21-Sep 11-Oct 31-Oct 20-Nov 10-Dec 30-Dec 19-Jan
307-NJ 307-CE Cement & Asphalt 307-IA ODOT 304 Treated Beginning Date of Instruments Continuous Monitoring Mode
Figure 8.4 Construction and instrumentation time line at the site
For each section, 100% of the precipitation was applied for those durations during which no surface protection exists to match the initial measured moisture content.
Subsequently, starting from the first day after paving the 1st asphalt layer, percent of precipitation that goes into soil was reduced to 25 percent to give a better match with the observed field values.
The comparison between measured and FEM predicted moisture content is shown in Figures 8.5 and 8.6 for an unbound (307-IA) section and a bound (asphalt treated) sections, respectively. The moisture content variation in subbase and subgrade layer could be used as an indication of effectiveness of base layer to drain out water that infiltrates into pavement from precipitation. This figure shows that a good match between
FEM prediction and field measured volumetric moisture content was obtained.
Upon closer examination of Figures 8.5 and 8.6 and analyses of other sections, one can observe that in all sections the measured water contents in the base and subgrade soils remained fairly steady. The observed trend of increasing water content during the 217
spring season manifested the fact that this period is the most critical season for the pavement structures. FEM predictions show close match with field measured results for most of the simulation period. The exception to this is after the thawing period in 2004, where some discrepancy was observed. To enhance the FEM models to simulate field conditions, considerations of the effects on material properties due to other climate factors such as temperature and freezing-thawing within pavement layers, should be made in the future study.
Figure 8.5 Comparison between measured and FEM predicted volumetric moisture contents for 307-IA section
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Figure 8.6 Comparison between measured and FEM predicted volumetric moisture contents for asphalt treated section.
8.4 Parametric Study
To study the effect of rate of infiltration into the pavement sub-layers as well as variations in the ground water table on water flow through a typical flexible pavement configuration, the FEM model based on the calibrated ATB-90 materials properties and pavement layers configurations were used for a parametric study.
8.4.1 Effect of Precipitation Infiltration Rate
In order to evaluate the effect of infiltration rate on the drainage characteristics of pavement layers, precipitation rate equal to 100, 75, 50 and 25 percent of total precipitation was applied to an unbound section (307-NJ). Average moisture contents computed for the entire simulation period in base, subbase and subgrade due to different infiltration rate are shown in Figure 8.7. The volumetric moisture content for all cases 219
remained practically constant with no significant variation noticed in base and subbase layers. For subgrade layer, the figure shows that an increase in infiltration rate results in an increase of moisture content. However, the variation is too small to warrant special consideration in FEM modeling. Note that the subgrade represents only the upper I ft of subgrade soil; going deeper in subgrade layer, the FEM analysis results show that the infiltration rate has negligible effect.
0.4
0.35
0.3
0.25 307-NJ base 0.2 Subbase Subgrade 0.15 Volumetric water content 0.1
0.05
0 100% 75% 50% 25% Infilitration percent
Figure 8.7 Volumetric water content at 307-NJ section under different precipitation rates
8.4.2 Influence of Water Table
The initial position of the water table in the FEM model is used to establish the water head conditions for the system at the beginning of each simulation. Three water
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table depths at 6, 8.5 and 12 feet below the asphalt surface were simulated to exam the influence of ground water table on the moisture content.
Figure 8.8 shows the FE computed volumetric water content of top 1 ft layer of subgrade materials for different water table positions in a cement treated base section.
When the water table is lowered, the suction for the soils above it is increased.
Consequently, the lower the water table, the lower the volumetric water content in the subgrade.
cement treated-passing lane
0.4 0.38
0.36
0.34
0.32 0.3
0.28 12 feet 0.26 6 feet 8.5 feet
volumetric moisture content moisture volumetric 0.24
0.22
0.2 9/6/03 1/5/04 5/5/04 9/3/04 1/2/05 5/3/05 9/1/05 12/31/0 5/1/06 time(day) 5
Figure 8.8 Volumetric water content of subgrade soils at cement treated base section under different water table depth
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8.4.3 Drainage Efficiency of Permeable Base
The hydraulic design of a permeable base in the M-E pavement design procedure is based on the time-to drain principle. The main parameter of interest in the time-to- drain approach is the time required to drain the permeable base to a pre-established moisture level. The design standard rates the permeable base drainage quality from
“Excellent”, if time to drain is limited to two hours, to “Very Poor” if it doesn’t drain at all (MEPDG, 2004 ). .
Transient flow analysis was conducted to study the time required for different permeable base materials to drain 50 % of water. Specific to the measured laboratory permeability of the materials and the pavement geometry (width of the road, distance from the edge of the pavement to the edge drain, transverse slop, permeable layer thickness) of the ATB-90 project, the performance of the different permeable base materials in the field is evaluated. Initial condition was assigned to be the same as those used in the calibrated model; i.e., a steady state analysis was made to obtain the conditions where all the layers were fully saturated. Then, transient analysis was performed to estimate the time required for the permeable base material to change in water content from fully saturated to 50% saturation.
The design guidelines (ME Design Guide, 2004) rate the permeable base drainage quality from “Excellent” to “Very Poor.” according to Table 8.2. To evaluate the drainage efficiency of different permeable base materials, calculation of time to drain was performed using both saturated and unsaturated flow theory. The FHWA’s computer program (DRIP) was used to calculate time to drain based on saturated flow condition, whereas the SEEP/W program was used for the unsaturated flow calculations. 222
Table 8.2 Permeable base quality of drainage rating based on time taken to drain 50 percent of the permeable water (M-E design guide, 2004) Quality of Drainage Time to Drain Excellent 2 hours Good 1 day Fair 7 days Poor 1 month Very Poor Does not drain
Depending on the measured laboratory permeability of the materials, effective porosity, and the highway geometry (width of the road, distance from the edge of the pavement to the edge drain, longitudinal slope, transverse slop, permeable layer thickness) of the project. The performance of the permeable base material in the field can be evaluated using DRIP software.
Depending on the baseline configuration of the ATB-90 project, transient flow analysis was made to study the required time to drain for different permeable base materials. Results of FEM analysis on time to drain to reach 50% saturation based on the laboratory determined hydraulic conductivity are presented in Table 8.3 and Figure 8.9. It can be seen that the open graded material would provide an excellent drainage performance, as they will reach 50 degree of saturation within the time recommended.
The dense graded material (304 Fine grading) and 307 materials (NJ, IA and CE) fine gradation needs a longer time to reach 50% saturation, thus increasing the chances of saturation of the subgrade soils.
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Table 8.4 Drainage performance of permeable materials (ATB-90) Saturated Time to Drain-(50% Unsaturated Time to Drain-(50% Materials saturation criteria)-hours saturation criteria)-hours 2.75-Good 304-Fine 5 days- Fair 0.59-Excellent 304-Median 1.35- Excellent 0.17-Excellent 304-Coarse 0.55- Excellent 307 – NJ Fine 0.75-Excellent 10.04-Good 307 – NJ Median 0.47-Excellent 0.85- Excellent 307 – IA Fine 1.58-Excellent 7.1-Good 307 – IA Median 0.6-Excellent 1.20- Excellent 307 – CE Fine 0.72-Excellent 4.6-Good 0.53-Excellent 307 – CE Median 0.57- Excellent 0.12-Excellent No. 57 0.05-Excellent 0.14-Excellent Cement Stabilized 0.06-Excellent Asphalt Stabilized 0.14-Excellent 0.06-Excellent
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100 asphalt treated Cement Treated 90 AASHTO No. 57 304-F 304-M 80 304-C 307-NJ-F 307-NJ 70 307-IA-F 307-IA 307-CE -F 60 307-CE
Saturation % Saturation 50
40
30
20 0.01 12 100 10000 Time (hr)
Figure 8.9 Time to drain for 50% saturation required for different permeable base materials.
Figure 8.10 shows that specific to the ATB-90 pavement design, a permeability of
1000 ft/day will not necessarily result in a time to drain equal to 2 hours. Therefore, design of permeable base should not only based on material hydraulic conductivity. An efficient drainage relies on a consideration of factors such as pavement geometry (width of the road, distance between the edge of the pavement to the edge drain, longitudinal and transverse slopes, permeable layer thickness) and material hydraulic functions ( SWCC and hydraulic conductivity).
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1000
100
10 M-E design guide recommendation 2 Time (hr) 1
0.1
0.01 01000 5000 10000 15000 20000 25000 30000 Permeability (ft/day)
Figure 8.10 Time to drain for 50% saturation for different permeable base materials with different permeabilities
8.5 Design Consideration of Permeable Base Layer
Since it is not possible to conduct simulations that would include combination of materials, geometries and boundary conditions to cover all possible drainage configurations. Therefore, a representative (baseline) model should be defined.
Simulations with the baseline model are used to gain insight into the role of unsaturated flow in pavement drainage, and modifications to the model permit a parametric study of the drainage response to be investigated for alternative conditions and configurations.
The baseline configuration in this chapter simulates the configuration of a ATB-
90 highway pavement section.
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The design study only consider different types of permeable base materials, permeable base hydraulic conductivity, thickness of permeable base layer, subbase hydraulic conductivities, type and location of underdrain pipes, and shoulder slope. Other important factors, such as asphalt permeability, characteristics of the surface cracks, subgrade properties, are not included in the design study.
It should be noticed that the analysis was made only on the permeable base layer, with no consideration of the effect of underlaying layers. The saturated analysis made with DRIP is considered only that the movement of water will be laterally and there is no downward movement of water. FEM analysis using the SEEP/W software can give more realistic analysis with consideration of the effect of layer configuration and water movement direction.
One important factor of unsaturated flow is the hydraulic conductivity functions.
The effect of hydraulic conductivities as a function of suction for different ODOT 304 gradation is shown in Figure 8.11. As it can be expected, the hydraulic conductivity of all soils decrease as they become less saturated. As shown by SWCC, a decrease in water content leads to an increase in suction that will result in a decrease of unsaturated hydraulic conductivity. Figure 8.11 shows that the amount of change is not the same for all the gradation, the coarser-grained soils become less conductive than the finer-grained soils at the suction heads of only about 2 kPa. The results here demonstrate the effect of
SWCC’s parameters on the drainage performance of open graded materials.
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1000 100 10 1 0.1 0.01
0.001 coarse gradation 0.0001 med gradation fine gradation hydraulic conductivity (cm/s) 0.00001 0.000001 0.0000001 0.01 0.1 1 10 100 Suction (kPa)
Figure 8.11 Unsaturated hydraulic conductivity for ODOT 304 permeable base materials at different gradation.
8.5.1 Effect of Hydraulic Conductivity of Base Course Materials
To study the effect of hydraulic conductivity of base course materials, FE simulations were conducted using a wide range of saturated hydraulic conductivity for base course materials. The simulations were identical to the baseline geometry except that the saturated hydraulic conductivity for base course was varied.
Figure 8.12 shows that the variation of degree of saturation with time for different hydraulic conductivities of base material.
Figure 8.13 shows the correlation between the times required to 50% drain and the hydraulic conductivity, from which a 0.45 cm/sec (1275 ft/day) hydraulic conductivity is needed. Saturated flow analysis using DRIP software shows that a 0.315
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cm/s (890 ft/day) for the baseline configuration for base materials is needed to meet the criteria.
Excellent Good Fair Poor Very Poor 100 2 cm/s 90 1.0 cm/s 0.75 cm/s 80 0.5 cm/s 70 0.25 cm/s 0.1 cm/s 60 0.01 cm/s 50 1e-3 cm/s
Saturation % 0.05 cm/s
40 1.0e-4 cm/s
30 1e-5 cm/s 1.0e-6 cm/s 20
10 0.1 1 10 100 1000 10000 Time (hr)
Figure 8.12 Effect of hydraulic conductivity of permeable base on drainage performance
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10
1
0.1
0.01
0.001 permeability (cm/s) permeability 0.0001
0.00001
0.000001 0.1 1 10 100 1000 10000 Time (hr)
Figure 8.13 Effect of hydraulic conductivity of permeable base materials on drainage performance
8.5.2 Effect of Permeable Base Layer Thickness
The impact of the thickness of permeable base layer on pavement drainage performance was examined by FE. The FE for these different base layer thicknesses are shown in Figure 8.14. It appears that varying the thickness of permeable base layer would not improve drainage efficiency significantly of base layer.
Similar conclusion was stated in the M-E Design Guide that thickness of permeable base layer does not provide any significant influence on the drainage performance. 10 cm (4 inches) permeable base layer was recommended in the M-E design guide (2004).
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ODOT 307-NJ Section (Passing Lane) 100 D=10 cm (4 in) D=15 cm (6 in) 90 D=20 cm (8 in)
80
70
60 Saturation %
50
40
30 0.01 0.1 1 10 Time (hr)
Figure 8.14 Effect of thickness of permeable base layer on drainage performance
8.5.3 Effect of Subbase Hydraulic Conductivity
The impact of the subbase properties on pavement drainage performance was evaluated by FE simulations with a wide range of subbase hydraulic conductivity. The simulation results are shown in Figure 8.15, as it can be seen subbase hydraulic conductivity exerts significant influence on the pavement performance. For the subbase with high permeability, the subbase layer is able to accept higher percent of the water from the base course. This decreases the lateral flow of water through the permeable base layer.
To increase the effectiveness of the permeable base layer to drain water laterally to the underdrain pipe a separator layer is recommended. An impermeable layer, of aggregate
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material (treated or untreated) or geotextile or a combination of both, placed under the permeable base could enhance the pavement drainage performance.
ODOT 307-NJ Section (Passing Lane) 100 subbase permeability =10 cm/s subbase permeability = 1.0 cm/s 90 subbase permeability =0.1 cm/s subbase permeability = 1e-2 cm/s 80 subbase permeability = 1e-4 cm/s
70
60 Vol Water content 50
40
30 2 0.00 0.01 0.10 1.00 10.00 Time (hr)
Figure 8.15 Effect of subbase hydraulic conductivity on drainage performance of permeable base materials.
8.5.4 Pavement with No Underdrain Pipes
The underdrain provides a means for water to be removed from the pavement section. Without these components, water is removed from the pavement section by moving laterally to the exposed ditch face. FE simulations were conducted by considering two cases; the first case analyzes a pavement section without shallow underdrain pipes, while the second case analyzes a pavement section without deep
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underdrain pipes. The time to drain results for these two cases are compared to that of the baseline model in Figure 8.16 and 8.17.
ODOT 307-NJ Section (Passing Lane) 100 307-NJ (passing lane) 307-NJ no shallow pipe / passing lane 90 307-NJ (driving lane) 307-NJ no shallow pipe (Driving lane) 80
70
60 Vol Water content Water Vol 50
40
30 0.01 0.10 1.00 10.00 100.00 Time (hr)
Figure 8.16 Drainage performance of permeable base layer with no shallow underdrain pipe.
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ODOT 307-NJ Section 100 307-NJ (passing lane) 307-NJ/no deep trench/ passing lane 90 307-NJ/no deep trench/ (Driving lane) 307-NJ (driving lane) 80
70 Saturation % Saturation 60
50
40 0.01 0.10 1.00 10.00 Time (hr)
Figure 8.17 Drainage performance of permeable base layer with no deep underdrain pipe
As expected, without the shallow underdrain pipe, little water exits the system within the specified time. The free draining base material (hydraulic conductivity equal to
3800 ft/day) could not meet the time to drain criteria. To drain 50 percent of water the permeable layer needs 8 hours more than expected with the baseline model. Drainage efficiency is no longer dependent upon hydraulic functions of the base materials.
The situation is different for the second case. Simulations with no deep underdrain pipes do not show significant change in the time required to drain. The simulation results confirm the importance of the location of the underdrain pipes for removing water beneath pavement sections.
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8.5.5 Effect of Pavement Shoulder Slope
The lateral water movement in the base course is driven by two effects: gravity and suction gradients. When the downward moving water is impeded at the top of the impermeable cohesive subgrade, it then moves laterally down slope along the contact between subbase course and subgrade. The driving force for this flow is proportional to the slope of the contact between the subbase course and subgrade. Finite element analyses were performed for four shoulder slopes of 2%, 4%, 6% and 8%. The shoulder slope of the baseline model is 4%, The surface slope was left at 1.6%. The FE simulation results given in Figure 8.18 showed that the drainage performance of the permeable base layer can be improved by increasing shoulder slope.
ODOT 307-NJ Section (Passing Lane) 100 307-NJ 2% shoulder slope 307-NJ 4% shoulder slope 90 307-NJ 6% shoulder slope 307-NJ 8% shoulder slope 80
70
60 Saturation % 50
40
30 0 0.5 1 1.5 2 Time (hr)
Figure 8.18 Effect of shoulder slope on drainage performance of permeable base materials.
235
The simulation results match the observations made by other researchers, such as
Stormont and Zhou (2001) and Tan et al. (2004), for both of them suggested that the drainage performance increased with increasing cross slope. Increase the shoulder slope result in similar.
8.6 Summary and Conclusions
The results presented in this chapter showed that FEM can be used efficiently to simulate water flow through pavement layers. Two dimensional finite element software
(SEEP/W) was used to model the unsaturated flow in pavement. Whereas FHWA software (DRIP) was used to conduct the saturated water flow. Since saturated flow assumption fails to take into consideration the effect of the variation of the hydraulic conductivity with volumetric water content or suction, the time to drain evaluated on the basis of traditional “time to drain” equations can result in an unreal short time to drain compared to calculations based on unsaturated flow conditions. FEM analysis using the
SEEP/W software gives more realistic analysis than DRIP software, mainly due to considerations of the layer configuration and water movement direction.
FEM modeling taking into consideration of both saturated and unsaturated flow
(e.g., SEEP/W) appears to be capable of predicting water content regime in pavement layers (base, subbase, and subgrade), as demonstrated in this paper when comparing with field moisture monitoring results. Accurate simulation of the boundary and initial conditions, together with representative soil water characteristic curves and the hydraulic conductivity curves for each material, results in accurate prediction that match with the measured field results. 236
FE simulation results showed that the moisture content in the permeable base layers after reaching the equilibrium condition will not be affected by the infiltration rate.
Water table location has more significant effect on the moisture content of subgrade layer than the infiltration rate.
Based on both FEM modeling and laboratory test results, the bound base materials exhibit the best ability to drain out the infiltrated water than the unbound base materials used in this study. The dense graded materials (304 Fine gradation) and 307 materials (NJ, IA and CE) fine gradation need a longer time to reach 50% saturation, that result in increase in the chances of saturation of the subgrade soils.
FE simulation results showed that a single value of 1000 ft/day of hydraulic conductivity is not necessarily appropriate to obtain a time to drain 50% of water within 2 hours. An efficient drainage relies on a consideration of factors such as pavement geometry (width of the road, distance between the edge of the pavement to the edge drain, longitudinal and transverse slopes), and material hydraulic functions (SWCC and hydraulic conductivity).
Analysis made on baseline configuration and 307-NJ permeable base materials showed that a hydraulic conductivity of 0.45 cm/sec (1275 ft/day) is sufficient to obtain time to drain equal to 2 hours. The saturated flow analysis using DRIP software shows that 0.315 cm/s (890 ft/day) is needed.
The design of permeable should not only based on material hydraulic conductivity; other factors such as pavement geometry and material hydraulic functions (
SWCC and hydraulic conductivity) should be considered.
237
The drainage performance of the permeable base materials is affected by the subbase properties. The lower the hydraulic conductivity of the subbase, the more it will slow downward flow in the base course and encourage lateral flow toward an outflow point. To increase the effectiveness of the permeable base layer to drain laterally the infiltrated water to the underdrain pipe, the use of a separator layer is recommended.
The thickness of permeable base layer does not have a significant effect on the time to drain parameter. Therefore, a value of 10 cm (4 inches) is recommended for the thickness of permeable bases.
An increase in shoulder slope is effective in increase drainage efficiency for permeable base materials.
Location of the underdrain pipes for removing water beneath pavement sections affects the drainage performance. The shallow underdrain pipes (at the level of base layer) exerts higher effect than the deep underdrain pipe.
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CHAPTER IX
CONCLUSIONS AND RECOMENDATIONS
9.1 Summary
Towards the objective of evaluating the performance of different permeable base materials used by Ohio Department of Transportation (ODOT), a series of laboratory work, field work and theoretical analysis have been carried out. A detailed literature review was performed to quantitatively evaluate the pavement performance under the effects of environmental conditions together with the evaluation of drainage efficiency of different permeable base materials used in this project. Correlations between field observations and laboratory measurements were made to afford insights into the interactions between environmental parameters, structural properties, and pavement performance. Pertinent laboratory test results on these permeable base materials as well as field monitoring data and performance test results were presented. The effectiveness of the five permeable base materials and ODOT 304 controlled fill material was studied based on extensive use of the mechanical properties of materials obtained in the laboratory, field measured daily environmental data, and pavement response data. Results obtained help to assess the impacts of the presence of the permeable base layer on the variation of moisture in the subgrade and on the overall structural capacity of the
239
pavement structure. A comparison between the backcalculated and laboratory determined moduli was presented for different base course materials and for subgrade soils. Seasonal adjustment factors for the subgrade soil were estimated based on the collected moisture data at ATB-90 project.
The Enhanced Integrated Climatic Model (EICM) software was used to predict temperature, moisture and frost depth data at the ATB-90. Project Site. Weather data obtained from an automated weather station, such as precipitation, temperature, wind speed and percent sunshine was used as input in the model. Materials properties of six different base materials, subbase and subgrade was also entered as a part of the model materials input. Comparisons were made between the predicted and measured moisture contents and temperature along the depth of pavement sections as well as frost depth at different times during the simulation period.
A new predictive model incorporating the matric suction and stress as state variables for estimating the resilient modulus of cohesive soils at different levels of moisture content and state of stresses was presented. The accuracy of the proposed model is validated against experimental data of A-4, A-6 and A-7 soils conducted by the authors as well as by other data available in the literature. The proposed model was shown to compare well with the experimental data as well as the empirical equations presented in the M-E Design Guide accounting for the influences of moisture content and states of stresses on the resilient modulus.
The characterization of open graded materials in the Mechanistic-Empirical
Pavement Design Guide (MEPDG) was reviewed, and this characterization was applied to different Ohio permeable base materials. Flexible pavement designs and rut
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performance derived from the MEPDG approach were compared for different base materials over a range of AC thickness, base materials, subgrade and other material properties. The impact of the most significant parameter in each group on rut based service life was evaluated: thickness design (asphalt concrete thickness), environment
(moisture content variation), and material properties (base and subgrade resilient modulus).
Movement of water through the six pavement sections at ATB-90 project site was modeled by the finite element method. A finite element based numerical model of flow through flexible pavements was developed based on actual pavement geometries and material characteristics. The two dimensional finite element software (SEEP/W) capable of simulating unsaturated water flow in layered systems was used. Parametric study was performed to exam the effect of precipitation infiltration rate and ground water table elevation on the predicted moisture contents in base, subbase and subgrade specific to
ATB-90 project. FEM analysis was also performed to evaluate the drainage efficiency of the bound and unbound permeable base materials in terms of time required for reaching
50% saturation. The effects of unsaturated conditions on pavement drainage due to pavement materials characterization and pavement configuration (geometry, layers, drainage systems) were evaluated. Parametric study was conducted to study the effect of different parameters on the drainage performance of permeable base layer.
The study results on permeable base materials provided essential information to assist ODOT engineers in selecting the best materials with good performance characteristics. Also, the study results provided important information to help engineers in assessing the performance of the pavements at ATB-90 project site.
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9.2 Conclusions
Based on the research work performed in this study, the following conclusions can be drawn:
1. The Enhanced Integrated Climatic Model was shown to be capable of
predicting the expected moisture and temperature variations that the pavement
was subjected to. One of the main deficiencies of the EICM was the prediction
of pavement surface temperature. The capability of the EICM to predict
pavement frost depth was shown to be good for the pavement sections built
with unbound permeable base materials. On the other hand, it was difficult to
achieve good predictions for pavement sections built with bound base
materials.
2. A good agreement between predicted and measured moisture content profiles
was found, especially for the base and subbase materials. For subgrade
materials, a small variation between model predicted results and field measured
results was found; however, it was shown that enhancing the ability of the
model to predict moisture content within the subgrade can be done by
modeling the subgrade layer with multi sub-layers, with each layer properties
obtained from field measured properties, such as dry density, initial moisture
content and grain size distribution.
3. FEM modeling taking into consideration of both saturated and unsaturated flow
(e.g., SEEP/W) appears to be capable of predicting water content regime in
242
pavement layers (base, subbase, and subgrade). Accurate simulation of the boundary and initial conditions, together with representative soil water characteristic curves and the hydraulic conductivity curves for each material, results in accurate prediction that match with the measured field results. The sub-items for this conclusion include:
• FE simulation results showed that the moisture content in the permeable
base layers after reaching the equilibrium condition will not be affected
by the infiltration rate. Water table location has more significant effect
on the moisture content of subgrade layer than the infiltration rate.
• Since saturated flow assumption fails to take into consideration of the
variation of the hydraulic conductivity with volumetric water content or
suction, the time to drain evaluated on the basis of traditional “time to
drain” criteria equations can result in an unreal short time to drain
compared to calculations based on unsaturated flow conditions.
• FE simulation results showed that a single value of 1000 ft/day of
hydraulic conductivity is not necessarily appropriate to obtain a time to
drain 50% of water within 2 hours. An efficient drainage relies on a
consideration of factors such as pavement geometry (width of the road,
distance between the edge of the pavement to the edge drain,
longitudinal and transverse slopes), and material hydraulic functions
(SWCC and hydraulic conductivity).
243
• Based on both FEM modeling and laboratory test results, the bound
base materials exhibit the best ability to drain out the infiltrated water
than the unbound base materials used in this study. The dense graded
materials (304 Fine gradation) and 307 materials (NJ, IA and CE) fine
gradation need a longer time to reach 50% saturation, that would result
in increase in the chances of saturation of the subgrade soils.
• Analysis made on baseline pavement configuration showed that a
hydraulic conductivity of 0.45 cm/sec (1275 ft/day) is sufficient to
obtain time to drain equal to 2 hours. The saturated flow analysis using
DRIP software shows that 0.315 cm/s (890 ft/day) is needed.
• The drainage performance of impervious the permeable base materials
is affected by the subbase properties. To increase the effectiveness of
the permeable base layer to drain laterally the infiltrated water to the
underdrain pipe, the use of a separator layer is recommended.
• An increase in shoulder slope is effective in increase drainage
efficiency for permeable base materials.
• Location of the underdrain pipes for removing water beneath pavement
sections affects the drainage performance. The shallow underdrain
pipes (at the level of base layer) exerts higher effect than the deep
underdrain pipe.
4. Based on the concept of effective stress of unsaturated soils, the matric suction
of soil was shown to be an important state variable for predicting moisture-
244
dependent resilient modulus of cohesive soils. The proposed prediction model
was shown to be capable of predicting the resilient modulus of cohesive soils
over a range of stress states and water content. A good correlation was found
between the proposed model and the empirical model adopted in the new
MEPDG to account for the variation of resilient modulus with variation of
degree of saturation.
5. Analysis of pavement performance using the new mechanistic-empirical
pavement design guide to study the impact of open graded base materials on
pavement performance showed unexpected behavior. It was found that
variations in permeable base stiffness had little influence on permanent
deformation. This unexpected behavior may be a consequence of the
simplifications implicit in linear elastic modeling of pavement materials. Due
to the much higher asphalt concrete stiffness, varying the base thickness did not
affect the vertical compressive strain in the asphalt concrete layer.
6. Subgrade resilient modulus had more impact on permanent deformation than the
base resilient modulus. Variations of asphalt concrete thickness, on the other
hand, were found to have a much more significant impact rut on performance
prediction. These results suggest that, in the MEPDG methodology, the design
process for rut performance is dominated by the asphalt concrete layer.
7. The sensitivity of service life to the specified design criteria showed that the
MEPDG procedure can be used as an effective tool to evaluate the impact of
different design criteria on performance and service life of pavement structures.
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9.3 Recommendations for Future Study
The following tasks could be undertaken to enhance the current research results and findings.
-ODOT may consider to re-evaluate ODOT 304 gradation requirement for use as base materials in pavement construction. The current gradation of ODOT 304 permits the presence of fines up to 15%. If the amount of fines in ODOT 304 is 15%, the permeability may be too low as effective drainage materials. Re-evaluation of the amount of fines allowed in ODOT 304 materials specification is warranted, if ODOT 304 is to be considered as drainable base materials
-The monitoring of the long-term performance of the instrumented pavement sections needs to be continued with FWD testing and pavement surface profile measurement.
These non-destructive measurements would provide quantitative data for assessing long- term performance of pavement built with different permeable base materials.
-A comprehensive life-cycle cost analysis needs to be conducted to truly distinguish the cost benefits of the various permeable base materials that ODOT can use in the future.
-The ODOT 304 gradation curve regarding the amount of fines needs to be re-evaluated if the material was to be used as a drainage base material under the asphalt pavement.
Efforts should be directed toward minimizing the amount of fines in the materials while maintaining material stability.
-Continue check the validity of the EICM (Enhanced Integrated Climatic Model) to predict field measurement for ATB-90 project site, and conduct sensitivity analysis for each input parameter used in the model.
246
-More detailed analysis should be performed to investigate the relationship between the climatic condition, subsurface environmental data, and mechanical properties for pavement sections at the ATB-90 project sites. Seasonal shift functions as well as the seasonal adjust factors may be established to reflect the effects of the variations of moisture and temperature on the mechanical properties used in the M-E pavement design.
-The implementation of the NCHRP 1-37A methodology is a challenging task to state agencies. In addition to the local calibration needed to enhance the effectiveness and accuracy of the empirical performance models, large amounts of input data are required for routine designs. Material characterization inputs should be placed in a database for use in routine designs. As shown consistently in this study, continuing research is needed to better model the behavior of unbound materials in the pavement structure.
-The dynamic modulus of various asphalt concrete materials used at the project sites needs to be determined to enable ODOT to develop a database for future implementation of the mechanistic-empirical pavement design approach.
-Given the sensitivity of performance predictions to the asphalt concrete properties, level
1 characterization of typical HMA mixes should be a high implementation priority.
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