Deterioration Modelling of Granular Pavements for Rural Arterial Roads
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DETERIORATION MODELLING OF GRANULAR PAVEMENTS FOR RURAL ARTERIAL ROADS By Nahla Hussein Aswad Alaswadko Submitted in fulfillment of the requirements for the degree of Doctor of Philosophy Faculty of Science, Engineering and Technology Swinburne University of Technology Melbourne, Australia December 2016 ABSTRACT ABSTRACT To keep any network in service at an acceptable condition and maintain and preserve the network performance, the management system can be enhanced by models for predicting pavement conditions. Investigation into maintenance and rehabilitation of rural arterial roads is triggered when road condition reaches certain threshold levels of roughness, rutting and cracking. To assist road agencies in their long term planning, the aim of this research project is to develop powerful deterioration models for a rural arterial network, using novel approaches for data preparation and modelling. The reliability and usefulness of such models in a pavement management system stem from using accurate datasets with suitable modelling approaches. Therefore, the study’s main goal is to use a new approach for preparing accurate condition data to use in developing pavement deterioration models utilising a new modelling approach. Pavement condition parameters modelled herein, include surface roughness, rutting and cracking. To achieve the aim of this study, representative samples of highways from Victoria’s spray sealed rural network are considered. The selected sample network is from 40 highways with a combined length of more than 2,300 km. The network covers a large sample size with representative ranges of traffic loading, pavement strength, subgrade soil type and environmental factors for four road classes (M, A, B and C) which differ in quality and function. For each highway segment (100m), readily available historical time series data covering a number of years has been collected for use in models’ development and validation. A great emphasis and effort has been put into the data preparation process because it is a vital step for the development of robust deterioration models. Therefore, a State of the Art approach for preparing condition data for use in developing pavement deterioration models is demonstrated. It involves: data alignment process, data cleaning process, data filtering process, boundary limits of data and compiling and splitting datasets. The prepared panel datasets have hierarchical structure with four-levels of variation within the selected network. Time series observations are nested within segments which are nested within highways which are nested within the four road classes. I ABSTRACT An exploratory analysis has been carried out using traditional linear regression model. The Durbin-Watson test from this analysis is used to test whether the residuals are positively correlated or not. The results have indicated that there is statistical evidence that the residuals are positively correlated in all datasets. Consequently, this meant that the traditional regression approach is inappropriate for analysing panel data because it allows for a single level of variation only. The aim is to apply a modelling approach that captures the effect of variance at all possible levels in modelling roughness and rutting progression and predicting the probability of pavement crack initiation and progression. Multilevel analysis also called Hierarchical Linear Modelling (HLM) has been used to develop empirical deterministic models to predict pavement roughness and rutting progression over time as functions of a number of contributing variables. However, a Hierarchical Generalized Linear Models (HGLM) framework has been used to develop probabilistic models to predict crack initiation and progression. These types of analyses are used to allow for nesting of the data creating sample dependencies. This dependency violates the assumptions of traditional statistical models, including independence of errors and homogeneity of regression intercepts and slopes. Hence multilevel analysis can account for the correlation among time series data of the same segment and capture the effects of unobserved factors. As a result, the study demonstrates that unobserved heterogeneity is a critical aspect that should be considered not only between segments but between highways and road classes as well. The study presented predicted roughness and rutting progression models for the whole network and the four road classes. The study has concluded that a separate model for each road class provides more realistic predictions than the overall network model, which would help researchers to better understand the effect of contributing factors. The models will also help road agencies in developing more efficient maintenance programs. Accuracy and reliability of the developed models have been tested using simulation and validation processes. Further, assessments of the performance of all road classes are conducted by comparing their pavement conditions and factors affecting the rate of pavement deterioration. The results indicate that the effect of traffic loading is stronger than other factors on roughness progression for class M and class A; however, the effect of initial pavement strength is stronger than the other factors for class B and class C. II ABSTRACT Also, it has been found that the effect of time is stronger than the effects of other contributing factors on rutting progression for all road classes. Another main observation is that the decrease in strength of sealed granular pavements has a stronger contribution to rutting progression than the increase in traffic loading. The study estimates the probability of crack initiation at a certain time and predicts the probability of a pavement maintaining its current level of cracking. It has been found that the developed probabilistic model format for cracking data provide flexibility in the application of the model when triggers are set according to risk considerations. In addition, it is found that the effect of time is stronger than the effects of the other factors on crack initiation and progression. Also, the effect of traffic loading is stronger than the effect of initial pavement strength in crack initiation phase. However, the effect of pavement strength at any time is stronger than the effect of traffic loading in crack progression phase. The study results indicate that different pavement segments within a network may deteriorate at the same rate but their roughness values could be different at the same time due to their different initial pavement condition, design standards, construction quality, or any other unobserved variables. Further, it has been observed that subgrade soil type and climate condition only affect roughness and rutting progressions of light duty pavements. The study also showed that class M roads have longer gradual phase of roughness progression than the other road classes. However, they have shorter rutting gradual phase than the other road classes. From cracking observation data, the study has shown that there are less cracked observations in heavy duty pavements than light duty pavements due to more frequent crack sealing practice for the former pavements than the latter. Further, in all road classes the percent of observations of ‘insignificant affected area’ is higher than for the other categories, whereas the percent of observations of ‘significant affected area’ is lower than the other categories. Also, it shows that observations of the significant affected area for class C are higher than for the other road classes. The study concludes that multilevel modelling approach is a successful approach to present advanced analysis of pavement deterioration models. The procedure outlined is quite general, and can be applied to any pavement condition variable that has continuous data or ordinal classification with data that has a hierarchical structure. With III ABSTRACT accurate prediction models, the implications of optimum maintenance timing and rehabilitation strategies can be assessed with confidence and practical decisions can be made. IV ACKNOWLEDGMENTS ACKNOWLEDGMENTS This thesis would never have done without the guidance, encouragement and support from many people. Special thanks must go to my main supervisor, A/Prof. Rayya Hassan for her continuous guidance and encouragement. Her support and enlightening discussions were essential for this research. This thesis would never have been possible without her valuable advice, critical review and useful suggestions. I would also like to thank my associate supervisors, A/Prof. Denny Meyer for her helpful discussions in using HLM7 software and her recommendations in statistics, and Dr. Robert Evans for his early assistance with using In-House Excel based tool. I would acknowledge the Iraqi Ministry of Higher Education and Scientific Research for granting me the scholarship to give me the opportunity to undertake this study research. The financial support provided by the Iraqi government for supporting this study is gratefully appreciated. I acknowledge and appreciate Prof. Riadh Al-Mahaidi for his continuous support and encouragement, Dr. Sylvia Mackie for her always willing to help me with editing, VicRoads for supplying the data for this thesis, and Mr. Hunar Hamza and A/Prof. Everarda Cunningham for their early help with statistics. I wish to express my love and deep gratitude to my husband, Bayar Mohammed, a friend who made everything possible to hold my hands through the smooth and the rough patches of this road.