Prediction and Classification of Expansive Clay Soils 25 AGUS SETYO MUNTOHAR

Expansive Soils BALKEMA - Proceedings and Monographs in Engineering, Water and Earth Sciences Expansive Soils

Recent advances in characterization and treatment

Editors Amer Ali Al-Rawas Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, Sultanate of Oman Mattheus F.A. Goosen School of Science and Technology, University of Turabo, Puerto Rico, USA

This edition published in the Taylor & Francis e-Library, 2006. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” All rights reserved. No part of this publication or the information contained herein may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic or mechanical, by photocopying, recording or otherwise, without written prior permission from the publishers. Although all care is taken to ensure integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers nor the author for any damage to property or persons as a result of operation or use of this publication and/or the information contained herein. Published by: Taylor & Francis/Balkema P.O. Box 447, 2300 AK Leiden,The Netherlands e-mail: [email protected] www.balkema.nl, www.tandf.co.uk, www.crcpress.com British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data Expansive soils: recent advances in characterization and treatment / editors: Amer Ali Al-Rawas, Mattheus F. A. Goosen. p. cm. Includes index. 1. Soil consolidation. 2. Swelling soils. I. Al-Rawas, Amer Ali. II. Goosen, Mattheus F.A. TE210.4.E96 2006 624.1'5136–dc22 2005035532

ISBN10 0–415–39681–6 ISBN13 978–0–415–39681–3 Contents

List of contributors ix Preface xi

PART 1 Nature, identification, and classification of expansive soils 1

1 Geology, classification, and distribution of expansive soils and rocks: a case study from the Arabian Gulf 3 AMER A. AL-RAWAS, MATTHEUS F.A. GOOSEN, AND GHAZI A. AL-RAWAS

2 Identification and classification of expansive soils 15 SUDHAKAR M. RAO

3 Prediction and classification of expansive clay soils 25 AGUS SETYO MUNTOHAR

4 Overview of mineralogy of bentonites: genesis, physicochemical properties, industrial uses, and world production 37 RICHARD PRˇ IKRYL

5 Swelling in non-vertisolic soils: its causes and importance 55 MIGUEL ANGEL TABOADA AND RAÚL SILVIO LAVADO

PART 2 Volume change characteristics 79

6 ESEM study of structural modifications of argillite during hydration/dehydration cycles 81 JOËLLE DUPLAY, GERMAN MONTES-HERNANDEZ, AND LUIS MARTINEZ vi Contents

7 Large-scale odometer for assessing swelling and consolidation behavior of Al-Qatif clay 85 SHAHID AZAM

8 Water sorption and dilatation of bentonites and montmorillonite-rich clays 101 RADEK HANUS, IRENA KOLARÍKOVÁ,ˇ AND RICHARD PRIKRYLˇ

PART 3 Swelling potential measurement 115

9 ESEM–DIA method to estimate swelling–shrinkage of raw and cation-saturated bentonite 117 GERMAN MONTES-HERNANDEZ

10 Effect of remolding techniques on soil swelling and shear strength properties 127 MOUSA F. ATTOM, MAJED M. ABU-ZREIG, AND MOHAMMED TALEB OBAIDAT

11 Swelling rate of expansive clay soils 139 ROSLAN HASHIM AND AGUS SETYO MUNTOHAR

12 Swelling behavior of Ankara Clay: predictive techniques, damage details, and swelling maps 149 ZEYNAL ABIDDIN ERGULER AND RESAT ULUSAY

13 Prediction of swelling characteristics with free swell index 173 BHYRAVAJJULA R. PHANIKUMAR

PART 4 Advanced techniques for swelling potential assessment 185

14 Remote sensing of expansive soils: use of hyperspectral methodology for clay mapping and hazard assessment 187 SABINE CHABRILLAT AND ALEXANDER F.H. GOETZ

15 Spectroscopy as a tool for studying swelling soils 211 PATRICK CHEGE KARIUKI, KEITH SHEPHERD, AND FREEK VAN DER MEER

16 Finite element analysis of piers in expansive soils 231 YAHIA E.-A. MOHAMEDZEIN Contents vii

17 Prediction of swelling pressure of expansive soils using Neural Networks 245 YAHIA E.-A. MOHAMEDZEIN, RABAB IBRAHIM, AND ASSIM ALSANOSI

18 Shrinkage strain characterization of expansive soils using digital imaging technology 257 ANAND J. PUPPALA, SIVA PATHIVADA, VENKAT BHADRIRAJU, AND LAUREANO R. HOYOS

PART 5 Site characterization 271

19 Swelling behavior of expansive shale: a case study from Saudi Arabia 273 ABDULLAH I. AL-MHAIDIB

20 Volume change characteristics of compacted Ankara clay 289 ERDAL COKCA AND OZLEM CORA

21 Influence of trees on expansive soils in southern Australia 295 DONALD A. CAMERON, MARK B. JAKSA, WAYNE POTTER, AND AARON O’MALLEY

PART 6 Lime stabilization 315

22 Stabilization of expansive Ankara Clay with lime 317 MEHMET CELAL TONOZ, CANDAN GOKCEOGLU, AND RESAT ULUSAY

23 Lime stabilization of expansive clay 341 ZALIHE NALBANTOGLU

24 Combined lime and polypropylene fiber stabilization for modification of expansive soils 349 ANAND J. PUPPALA, EKARIN WATTANASANTICHAROEN, AND ALI PORBAHA

PART 7 Cement-stabilization 369

25 Assessment of anisotropic behavior of swelling soils on ground and construction work 371 EVANGELOS I. STAVRIDAKIS viii Contents

26 Stabilization of problematic soils using cement and lime 385 EVANGELOS I. STAVRIDAKIS

27 Influence of sand content on strength and durability of cement-acrylic resin treated soil 399 COSTAS A. ANAGNOSTOPOULOS

28 Physical and engineering properties of cement stabilized soft soil treated with acrylic resin additive 405 COSTAS A. ANAGNOSTOPOULOS

PART 8 Other treatment methods 417

29 Pozzolanic stabilization of expansive soils 419 P.V. SIVAPULLAIAH

30 Swelling characteristics and improvement of expansive soil with rice husk ash 435 AGUS SETYO MUNTOHAR

31 Effects of addition of fly ash on swell potential of an expansive soil 453 DEVRIM TURKER AND ERDAL COKCA

32 Dynamic characterization of chemically modified expansive soil 465 LAUREANO R. HOYOS, PHONLAWUT CHAINUWAT, AND ANAND J. PUPPALA

33 Assessment of seasonal effects on engineering behavior of chemically treated sulfate-rich expansive clay 483 LAUREANO R. HOYOS, ARTHIT LAIKRAM, AND ANAND J. PUPPALA

PART 9 Construction techniques and remedial measures 505

34 Granular pile-anchors: an innovative foundation technique for expansive soils 507 BHYRAVAJJULA R. PHANIKUMAR AND RADHEY S. SHARMA

Index 523 Contributors

Majed M. Abuzreig, Jordan University of Science and Technology, Irbid, Jordan Abdullah I. Al-Mhaidib, King Saud University, Riyadh, Saudi Arabia Amer Ali Al-Rawas, Sultan Qaboos University, Al-Khoud, Sultanate of Oman Ghazi A. Al-Rawas, Sultan Qaboos University, Sultanate of Oman Assim Alsanosi, University of Khartoum, Khartoum, Sudan Costas A. Anagnostopoulos, Aristotle University of Thessaloniki, Thessalonica, Greece Mousa F.Attom, Jordan University of Science and Technology, Irbid, Jordan Shahid Azam, University of British Columbia, Vancouver, Canada Venkat Bhadriraju, University of Texas at Arlington, USA Donald A. Cameron, University of South Australia, Australia Sabine Chabrillat, GeoForschungsZentrum (GFZ) Potsdam, Germany Phonlawut Chainuwat, PSA Engineering, Texas, USA Erdal Cokca, Middle East Technical University, Ankara, Turkey Ozlem Cora, Middle East Technical University, Ankara, Turkey Joelle Duplay, Centre de Géochimie de la Surface, Strasbourg, France Zeynal Abiddin Erguler, Hacettepe University, Ankara, Turkey Alexander F.H. Goetz, University of Colorado, USA Candan Gokceoglu, Hacettepe University, Ankara, Turkey Mattheus F.A. Goosen, University of Turabo, Gurabo, Puerto Rico Radek Hanus, Charles University, Prague, Czech Republic Roslan Hashim, University of Malaya, Kuala Lumpur, Malaysia Laureano R. Hoyos, University of Texas at Arlington, USA Rabab Ibrahim, Al-Amin Engineering Company, Khartoum, Sudan Mark B. Jaksa, University of Adelaide, Australia x Contributors

Patrick Chege Kariuki, International Livestock Research Institute (ILRI), Kenya Irena Kolaˇríková, Charles University, Prague, Czech Republic Arthit Laikram, University of Texas at Arlington, USA Raúl Silvio Lavado, Universidad de Buenos Aires, Argentina Luis Martinez, Universite Henri Poincare, Nancy, France Freek van der Meer, Delft University of Technology, Delft, The Netherlands Yahia E.-A. Mohamedzein, Sultan Qaboos University, Al-Khoud, Sultanate of Oman German Montes-Hernandez, Centre de Géochimie de la Surface, Strasbourg, France Agus Setyo Muntohar, Muhammadiyah University of Yogyakarta, Indonesia Zalihe Nalbantoglu, Eastern Mediterranean University, Gazimagusa, Mersin 10, Turkey Mohammed T. Obaidat, Jordan University of Science and Technology, Irbid, Jordan Aaron O’Malley, University of South Australia, Australia Siva Pathivada, University of Texas at Arlington, USA Bhyravajjula R. Phanikumar, GMR Institute of Technology, India Ali Porbaha, California Department of Transportation, USA Wayne Potter, University of South Australia, Australia Richard Pˇrikryl, Charles University, Prague, Czech Republic Anand J. Puppala, University of Texas at Arlington, USA Sudhakar M. Rao, Indian Institute of Science, Bangalore, India Radhey S. Sharma, Louisiana State University, USA Keith Shepherd, World Agroforestry Centre (ICRAF), Kenya P.V. Sivapullaiah, Indian Institute of Science, Bangalore, India Evangelos I. Stavridakis, Aristotle University of Thessaloniki, Greece Miguel Angel Taboada, Universidad de Buenos Aires, Argentina Mehmet Celal Tonoz, Hacettepe University, Ankara, Turkey Devrim Turker, Middle East Technical University, Ankara, Turkey Resat Ulusay, Hacettepe University, Ankara, Turkey Ekarin Wattanasanticharoen, University of Texas at Arlington, USA Preface

Expansive soils are a worldwide problem. The estimated damage to buildings, roads, and other structures built on expansive soils, for example, exceeds 15 billion dollars in the US annually. Such soils are considered natural hazards that pose challenges to civil engineers, construction firms, and owners. In some underdeveloped countries, buildings were constructed without any knowledge of the presence of expansive soils. This was in part due to a lack of historical evidence. With the rapid development in urban infrastructure, expansive soil problems have become more evident. There is therefore a need to address the problems associated with these soils. Expansive soils occur in many parts of the world but particularly in arid and semi-arid regions. In these regions, evaporation rates are higher than the annual rainfall so that there is almost always a moisture deficiency in the soil. The addition of water will cause ground heave in soils possessing swelling potential. Semi-arid regions are characterized by short periods of rainfall followed by long periods of draught causing cyclic swelling and shrinking phenomena. The ground heave that results from soil swelling potential is a multifactorial phenomenon that involves a combination of the type of material, type and amount of clay minerals, microfabric, initial moisture content, and initial dry density. Considerable research has been reported on expansive soils over the past three decades. The last international conference on expansive soils was held in Dallas, Texas, USA in 1992. The 6th International Conference on Expansive Soils was held in New Delhi in January 1988. Several textbooks on expansive soils are also available: Foundations on Expansive Soils by Chen, F.H., Elsevier 1988; Expansive Soils: Problems and Practice in Foundation and Pavement Engineering by Nelson, J.D. and Miller, D.J., John Wiley & Sons, Inc. 1992; Construction of Buildings on Expansive Soils by Sorochan, E.A., Aa Balkema January 1991; and Behaviour of Saturated Expansive Soil and Control Methods – Revised and Enlarged Edition by Katti, R.K./Katti, D.R./Katti, A.R., Routledge 2002. Since the most recent comprehensive publication is several years old, a book is now needed that updates the state-of-the-art knowledge in this area. This book provides a broad coverage of recent advances in the characteristics and treatment of expansive soils. There are nine parts each with specific chapters. It starts with an overview section (Part 1) on the nature, identification, and classification of expansive soils. Parts 2 and 3 deal with volume change characteristics and swelling potential measurements, respectively. Part 4 covers advanced techniques for swelling potential assessment. Such tests are important for assessing the actual swelling potential of the soil and estimating ground heave. Part 5 on site characterization presents field measurements of soil swelling potential and suction. The next three parts deal with lime stabilization, cement stabilization, and xii Preface other treatment methods. Chemical stabilization, for example, has gained wide attention as a successful technique for treating expansive soils. In the final section (Part 9), the performance of engineering structures built on expansive soils such as buildings, houses, embankments, and roads, is evaluated. Remedial measures used to address soil swelling problems are also described. The intended audience for this book includes researchers, practicing engineers, contractors, postgraduate and undergraduate students, and others working in expansive soils. The authors hope that the information provided in this book will help to promote a better understanding of expansive soils, contribute toward their treatment, and thereby reducing or minimizing their effects. The views expressed in the chapters of this book are those of the authors and do not necessarily reflect those of their respective institutions. The authors hope that this book will contribute to the advancement in research in expansive soils and help engineers in the development of practical solutions to expansive soil problems. Amer Ali Al-Rawas Sultan Qaboos University, Al-Khoud, Sultanate of Oman Mattheus F.A. Goosen University of Turabo, Gurabo, Puerto Rico 2005 Part 1 Nature, identification, and classification of expansive soils Chapter 3 Prediction and classification of expansive clay soils

Agus Setyo Muntohar1

Summary This chapter deals with the prediction and classification of the degree of expansiveness of clay soil. Statistics analysis was introduced as a simple technique for identifying and predicting the degree of swelling. There were three properties which were most strongly correlated to swelling potential (i.e. plasticity index, liquid limit, and clay fraction). In general, the models in the current study showed good correlation compared with previous models cited in the literature. The multiple linear regression model gave the best-fit for all soil conditions.

Introduction Expansive soils are a world wide problem (Seed et al., 1962; Kormonik and David, 1969; Al-Rawas et al., 1998; Alawaji, 1999; Cokca, 2001; Erguler and Ulusay, 2003; Muntohar and Hashim, 2003). Principally, swelling occurs when water infiltrates between the clay particles, causing them to separate. Several attempts have been made by researchers to obtain time-swell relationships for expansive soils. Some progress has been made toward characterizing swelling characteristics, despite the complexity of the behavior. Seed et al. (1962) reported that the time required for completion of swelling is relatively long. Many tests and methods have been developed for estimating shrink–swell potential. These include both indirect and direct measurements. Indirect methods involve the use of soil properties and classification schemes to estimate shrink–swell potential. Direct methods provide actual physical measurements of swelling. Several laboratory methods have been developed to directly determine the swelling a soil undergoes as the moisture content changes. These include free swell, expansion index, consolidation-swelling, California Bearing Ratio (CBR), potential volume change (PVC), and coefficient of linear extensibility (COLE). Currently, no one method of soil analysis estimates the shrink–swell potential accurately for all soils. Soil scientists recognize that shrink–swell behavior can be best predicted by examining a combination of physical, chemical, and mineralogical soil properties. Determining these properties and establishing a shrink–swell model that can be extrapolated

1 Department of Civil Engineering, Muhammadiyah University of Yogyakarta, Building F1, 3rd floor. Jl. Ringroad Selatan, Taman Tirto, Yogyakarta, Indonesia. 55183. Email: [email protected] 26 A.S. Muntohar across the same or similar parent materials is needed. Some researchers consider that this swelling potential can be linked to a single parameter. This chapter deals with the prediction and classification of the degree of expansiveness of clay soil. Statistics analysis is introduced as a simple technique for identifying and predicting the degree of swelling.

Potential of volume change Holtz and Gibbs (1956), Altmeyer (1955), Seed et al. (1962), and Daksanamurthy and Raman (1973) have evolved different methods to identify expansive soils based on the percentage of clay content, shrinkage limits (both volumetric and linear), plasticity index, liquid limit, and shrinkage index. Accordingly, they classified soils into low, medium, high, and very high degrees of potential expansiveness (Figures 3.1, 3.2, and 3.3). However, as

100 Swelling potential 80 Very high 60 High Mid 40 Low

20 Non Plasticity index, PI (%)

0 0 20 40 60 80 100 120 140 Liquid limits, LL (%)

Figure 3.1 Chart for potential expansiveness of soil. Source: Daksanamurthy and Raman, 1973.

100 Swelling potential 80 A = 2.0

40 Low Very high High Plasticity index (%) 20 Medium A = 0.5 Low 0 0 10203040506070 Clay content (%)

Figure 3.2 Potential expansiveness of expansive soil. Source: Williams, 1957. Classification of expansive clay soils 27

40 gh hi 32 Very

24 High

16 Medium Volume change (%)

8 Low Air dry to saturated condition under 1 psi load 0 20 40 0 20 4 0 0 81624 Colloid content Plasticity Shrinkage (% of = 0.001 mm) index (%) limit (%)

Figure 3.3 Relation of volume change to colloid content, plasticity index, and shrinkage limit. Source: Holtz and Gibbs, 1956.

Table 3.1 Classifications for degree of expansion (swelling potential)

Degree of Chen (1983) Seed et al. Daksanamurthy USBR (Holtz and expansion (1962) and Raman (1973) Gibbs, 1956)

Very high LL 60 PI 35 LL 70 CC 28 High 40 LL 60 20 PI 35 50 LL 70 20 CC 31 Medium 30 LL 40 10 PI 20 35 LL 50 13 CC 23 Low LL 30 10 20 LL 35 CC 13 with most soil systems, the activity classification scheme does not accurately estimate shrink–swell potential in mixed mineralogy soils. Parker et al. (1977) found that the activity index was too imprecise for both mixed and montmorillonitic mineralogy soils to be useful. However, Schreiner (1988) observed consistent trends in soil and bentonite/sand mixtures using the activity index as an indicator of shrink–swell potential. The classification of potential expansiveness does not give the same assessment of the swelling potential. It cannot conclude precisely the degree of volume change for particular soils as presented, for example, in Table 3.1. Seed et al. (1962) have also correlated the swelling potential with the degree of expansion values used by USBR as presented in Table 3.2. The boundaries defining these ranges are plotted in Figure 3.4.

Indirect estimation of swelling parameters In view of the difference that has been observed between the directly measured values of the swelling parameters and the values output by the earlier models, the first idea was to fit the models in question. The literature contains a considerable number of empirical techniques for assessing the swelling potential of soils, which correlated with consistency limits, moisture content, dry density, and depth of the soil samples (Seed et al., 1962; Chen, 1983; 28 A.S. Muntohar

Table 3.2 Classification of degree of expansion Degree of expansion Swelling potential (%)

Very high 25 High 5–25 Medium 1.5–5 Low 0–1.5

3 Activity

Swelling Potential = 50% Swelling Potential = 20% Swelling Potential = 10% Swelling Potential = 5% Swelling Potential = 1% 0 010203040 50 60 70 80 90 100 Percent Cloy Size (finer than 0.002 mm)

Figure 3.4 Classification chart for swelling potential. Source: Seed et al., 1962.

Basma et al., 1995; Djedid, 2001). Thomas (1998) proposed an expansive soil rating system, termed as the Expansive Soil Index (ESI). The model was developed as a function of using the soil properties most correlated with shrink–swell potential such as the ratio 2:1 between smectite and vermiculite minerals, swell index, liquid limit, and cation exchange capacity (CEC). The model gave expansive soil potential ratings (ESI) for each soil series. Seed’s model (Seed et al., 1962) and Chen’s model (Chen, 1983) are very simple. They used plasticity index parameters. Their models are given by Equations 3.1 and 3.2, respectively.

SP 60K (PI)2.44 (3.1) SP B eA(PI) (3.2) Classification of expansive clay soils 29

10 Seed, Woodward & Lundgren 1 Holtz & Gibbs 2 (Surcharge pressure 1 psi) (Surcharge pressure 1 psi) 9 Chen 3 (Surcharge pressure 1 psi)

5 1

Swelling potential (%) 4

3 3

4 1

Chen 4 (Surcharge pressure 5.94 psi)

0 5 10 15 20 25 30 35 40 Plasticity index (%)

Figure 3.5 Correlations between swelling potential and plasticity index. Source: Reproduced from Chen, 1983. where, SP is swelling potential plasticity index. Figure 3.5 shows the correlations between swelling potential and plasticity index that given by some researchers. K 3.6 105, A 0.0838, B 0.2558 are constants, and PI is plasticity index.

Data analysis Data used in this study consisted of 115 pairs and was compiled from many references (Seed et al., 1962; Kormonik and David, 1969; Al-Rawas et al., 1998; Alawaji, 1999; Cokca, 2001; Erguler and Ulusay, 2003; Muntohar and Hashim, 2003). Table 3.3 presents the data source Table 3.3 Number of data used for model Source of data Number of data

Current research data 7 Alawaji (1999a) 10 Attom et al. (2001) 3 Çokça (2001) 1 Zeynal and Ulusay (2003) 20 Seed et al. (1962) 12 USBR (quoted by Seed 28 et al., 1962) Total data 81

Table 3.4 Proposed empirical model for predicting swelling potential

No. Models Regression Regression statistics ANOVA

1 Variable: plasticity index (PI) R2 0.58 df 1 (a) SP 1.035(PI)0.816 Ad. R2 0.57 F 109.24 S 7.85 Pv 0.0001 (b) SP 10.106e0.056(PI) R2 0.444 df 1 Ad. R2 0.44 F 63.12 S 9.04 Pv 0.0001 (c) SP 2.231 0.453 (PI) R2 0.563 df 1 Ad. R2 0.56 F 102.08 S 9.04 Pv 0.0001 2 Variable: clay fraction (CF) R2 0.226 df 1 (a) SP 2.919(CF)0.535 Ad. R2 0.22 F 23.08 S 10.66 Pv 0.0001 (b) SP 11.418e0.0135(CF) R2 0.152 df 1 Ad. R2 0.15 F 14.22 S 11.16 Pv 0.0001 (c) SP 7.518 0.323(CF) R2 0.192 df 1 Ad. R2 0.18 F 18.77 S 10.89 Pv 0.0001 3 Variable: liquid limit (LL) R2 0.546 df 1 (a) SP 0.109(LL)1.236 Ad. R2 0.54 F 95.03 S 8.16 Pv 0.0001 (b) SP 6.871e0.0149(LL) R2 0.466 df 1 Ad. R2 0.46 F 69.10 S 8.85 Pv 0.0001 (c) SP 0.393(LL) 6.298 R2 0.56 df 1 Ad. R2 0.55 F 100.53 S 8.04 Pv 0.0001 4 Multiple linear regression: R2 0.613 df 3 SP (%) 0.171CF 0.0012LL R2 Adj. 0.60 F 40.608 0.409PI 1.869 S 7.64 Pv 0.0001

Notes Coefficient of confidence level () 0.05; SP is swelling potential (%); S: Standard error. Classification of expansive clay soils 31 that was used in the study. Preliminary statistics test was carried out for screening the variables used in the models. The variables, which only had good correlation with swelling potential, were chosen as independent variables. They were plasticity index (PI), liquid limit (LL), clay fraction (CF), dry density ( d), and water content (w). Due to the large variability of dry density and water content data, both variables were rejected as independent variables. Data analysis was considered in two stages (i.e. learning and validating). As much as 81 data samples were randomly used for formulating the empirical model in the learning stage. The other data was used for validating. The two most common empirical models, linear and nonlinear, were fitted to the data using a single independent variable. These were developed using SigmaPlot Ver 6.1. Multiple independent variables or multiple linear regression were also established for developing empirical models to indicate reliable assessment of swelling potential of a soil. The general models are given as follows:

● Linear (single): y b0 b1t ● b Power: y b0t 1

● (b1t) Exponential: y b0e ● Multiple linear: y b0 b1t1 b2t2 b3t3

The results of the statistical analysis are presented in Table 3.4.

Discussion

Empirical models The evaluation of swell behavior of a soil using undisturbed samples and specialized swell tests is a difficult and expensive process for practicing engineers and small builders. Therefore, there is a need for simple routine tests that can be performed on disturbed engineered samples to achieve the same purposes. The empirical models appearing in the literature are primarily related to prediction of swelling and swelling pressure from index properties of soils. Sometimes, the empirical models proposed cannot be applied appropriately to all soils due to different soil conditions and testing procedures. The data used, here, was compiled from different determinations of swelling and index properties. It was hoped that the models would be acceptable and generalized. Figure 3.6 shows the correlation between predicted-swelling and actual (measured) swelling. The figure plotted all the data used (i.e. 115 data samples were used in learning and validating). The dashed line shows the correlation between measured and predicted swell. It was expected that correlation should have lain on the 45 line (1 : 1 line), which refers to the colinearity of model. The figure illustrates that proposed empirical models, given in Table 3.3, give good correlations. The dash lines in Figures 3.6a, 3.6b, and 3.6c were laid down in the colinearity range of 0.5–0.8. The empirical model, proposed by Seed et al. (1962), as shown in Figure 3.6d, showed a very weak correlation in which the correlation was below line 0.5. It indicated that the proposed equation by Seed et al. (1962) is only appropriate for a measured-swelling of less than 30%. In our study, the multiple linear regression method (Equation 3.4, in Table 3.4) indicated a best-fit correlation. In general, the model can be used for all soil conditions. In the current study, multiple regression analysis was considered to derive an equation that can be used to predict swelling potential from several index and physical properties. The (a) (b) 50 Eq. 1(a) 1:1 50 Eq. 3(a) 1:1 0.8 0.8 40 40

30 30 0.5 0.5 20 20 Actual swelling (%) Actual swelling (%) 10 10

0 0 0 1020304050 0 1020304050 Predicted swelling (%) Predicted swelling (%) (c) (d) 50 Eq. 4 1:1 50 Seed et al. (1962) 1:1 40 0.8 40 0.8

30 30 0.5 0.5 20 20 Actual swelling (%) 10 Actual swelling (%) 10

0 0 0 1020304050 0 1020304050 Predicted swelling (%) Predicted swelling (%) (e) (f) 1:1 1:1 50 Eq. 1(c) 50 Eq. 1(b) 0.8 0.8 40 40

30 30 0.5 0.5 20 20

Actual swelling (%) 10 Actual swelling (%) 10

0 0 0 1020304050 0 1020304050 Predicted swelling (%) Predicted swelling (%) (g) (h) 50 Eq. 2(a) 1:1 50 Eq. 2(b) 1:1 0.8 0.8 40 40

30 30 0.5 0.5 20 20 Actual swelling (%) 10 Actual swelling (%) 10

0 0 0 1020304050 0 1020304050 Predicted swelling (%) Predicted swelling (%)

Figure 3.6 Correlation of proposed empirical model and actual swelling. Classification of expansive clay soils 33 use of multiple regression statistics is very important in reducing the number of variables that are considered to be an independent source of information. These variables are reduced to only 3 or 4 which adequately explains the variation in swelling properties of the soils. Many trials were carried out to correlate the swelling parameters to a combination of variables. The test of hypothesis of a linear model involved testing for significance of regression and testing on individual regression coefficients (Montgomery, 2001). For the proposed model, since P-value (Pv) is considerably smaller than the confidence level ( 0.05), the null hypothesis (H0: 1 2) was rejected, indicating a strong correlation between each variable. For all statistical models, single or multiple linear regressions, satisfactorily fulfilled the F-test. The coefficient of determination (R2) has been used as a global statistic to assess the fit of the model. However, this value will increase when a regressor is added. In this model, the R2 for a single variable is 0.192 (Equation 2c in Table 3.4). When added with two other variables it increases to 0.60. It showed significance. It can be concluded that swelling potential is linearly related to CF, LL, or PI. Testing of individual regression coefficient requires that at least one of the variables contributes significantly to the model. It has been observed that the CF and PI imply a significant contribution, since the standard error was 0.0661and 0.141 respectively, and the P-value was less than 0.05 (Table 3.5). The coefficients of variables lie in the range of 95% confidence level. The overall test indicated that the variables fulfilled the requirements of the t-test and F-test.

Classification degree of swell The empirical models have indicated that the multiple linear regression model gives the best-fit correlation for prediction of swelling potential of expansive soil. Furthermore, qualitative measurement is also needed to classify the degree of swelling. The measurement was determined based on the normal probability plot as shown in Figure 3.7. The measurement was simply divided into four regions based on the 25% percentile (Quartile), 50% percentile (Mean), and 75% percentile data. The classification of degree of expansiveness (swelling) is presented in Table 3.6. The determination is quite high compared to the category that was given by USBR in Table 3.2.

40 Very high 30

20 High

Swelling potential (%) Medium 10 Low 0 0 25 50 75 100 Sample percentile

Figure 3.7 Normal probability plot and qualitative measurement of swelling potential. 34 A.S. Muntohar

Table 3.5 Analysis of variance (ANOVA) multiple linear regression Variables Coefficients Standard error t-stat P-value Lower 95% Upper 95%

Intercept 1.8695 3.1612 0.5913 0.5559 8.1644 4.4253 CF 0.1707 0.0661 2.5804 0.0117 0.0389 0.3023 LL 0.00124 0.1320 0.00947 0.9925 0.2616 0.2640 PI 0.4092 0.1406 2.9105 0.0047 0.1292 0.6890

Table 3.6 Category for swelling potential classification

Proposed model USBR* Expansiveness remarks

SP 8.68 SP 1.5 Low 8.68 SP 15.1 1.5 SP 5 Medium 15.1 SP 28.8 5 SP 15 High SP 28.8 SP 25 Very high

Notes SP: swell potential (%); * Seed, et al. (1962).

Table 3.7 Predicted swelling potential and classification of soil used in the study

Soil code Clay fraction Liquid limit (LL) Plasticity index (PI) Predicted swelling Remarks (CF)% % % potential (SP)%

KB1 26.9 76.9 37.5 18.2 High KB2 30.0 89.7 47.5 22.8 High KB3 32.5 106.8 62.4 29.3 Very KB4 39.0 121.5 78.4 37.0 Very SB1 4.0 42.9 21.8 7.8 Low SB2 21.7 85.1 57.9 25.6 High SB3 47.0 138.3 95.1 45.2 Very

It can be noted that the models and classification devised in this study are a simple predictive tool for assessing swell potential of a given soil, both undisturbed and remoulded specimens. Using the proposed model (Table 3.5), the swelling potential of the soil used in this study can be predicted and then classified as presented in Table 3.7.

Conclusions Index and physical properties of soil are useful indicators to estimate engineering and swelling properties. There are three properties, which are most strongly correlated to swelling potential, PI, LL, and CF. The proposed models in the current study, showed good correlation compared with previous models cited in the literature. The multiple linear regression model gave the best-fit for all soil conditions. The classification of degree of expansiveness (i.e. swelling) has been well devised based on the statistical analysis. The degree of swelling can be classified into four distinct levels, low, medium, high, and very high. Classification of expansive clay soils 35

Acknowledgments The author gratefully appreciates the funding provided by the Ministry of Science, Technology, and Environmental (MOSTE) Malaysia through Intensify Research for Priority Area (IRPA) RM#8 and the University of Malaya through Short-Term IRPA Fund (Vot-F) 2002/2003. Sincere thanks go to Ir. Dr Roslan Hashim, Professor of University of Malaya, for his discussion.

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