Development of distress condition index of asphalt pavements using LTPP data through structural equation modeling |
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| Institution: | 1. Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China;2. Department of Highway and Railway Engineering, College of Transportation, Southeast University, 2# Sipailou, Nanjing, Jiangsu 210096, China;3. Department of Civil and Environmental Engineering, The University of Tennessee, 325 John D. Tickle Engineering Building, 851 Neyland Drive, Knoxville, TN 37996, United States;4. College of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China;1. 406 Town Engineering Building, Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA;2. 24 Town Engineering Building, Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA;3. 354 Town Engineering Building, Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA;4. 0147C Glenn L. Martin Hall, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA;1. Dep. of Engineering, University of Messina, Vill. S. Agata, C.da di Dio, 98166 Messina, Italy;2. Dep. of Civil and Environmental Engineering, National University of Singapore, 1 Eng. Drive 2, 117576 Singapore, Singapore;3. Dep. of Engineering, University of Messina, Vill. S. Agata, C.da di Dio, 98166 Messina, Italy;1. Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, USA;2. Center for Advanced Infrastructure and Transportation, Rutgers, The State University of New Jersey, USA;3. School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan, Hubei, China;1. Key Laboratory of Road Structure & Material, Ministry of Communication, PRC, Chang’an University, Xi’an 710064, China;2. Department of Civil and Environmental Engineering, Faculty of Engineering, University of Waterloo, Waterloo N2L 3G1, Canada;3. Sinohydro Bureau 7 Co. Ltd., Chengdu 610213, China;4. McIntosh Perry Consulting Engineers Ltd., Kingston K7P 0L8, Canada;1. School of Transportation, Southeast University, 2# Southeast University Road, Nanjing, Jiangsu 211189, China;2. National Engineering Laboratory for Advanced Rd. Material, Jiangsu Transportation Research Institute Co., Ltd, Nanjing, Jiangsu 211112, China |
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| Abstract: | Traditional pavement distress index such as the Pavement Condition Index (PCI) developed by U.S. Army Corps of Engineers determines coefficients of distresses based on subjective ratings. This study proposed an asphalt pavement distress condition index based on various types of distress data collected from the Long-Term Pavement Performance (LTPP) database through Structural Equation Modeling (SEM). The SEM method treated the overall distress index as a latent variable while various distresses were treated as endogenous and other influence factors such as age, layer thickness, material type, weather, environment and traffic, were exogenous observed variables. The SEM method modeled the contributions of various distresses as well as the influence of other factors on the overall pavement distress condition. Influences of age, layer thickness, material type, environment and traffic on the latent distress condition were in accordance with previous studies. Compared with previous attempts to model latent pavement condition index utilizing SEM method, more pavement condition measurements and influencing factors were included. Specifically, this study adopted the robust maximum likelihood estimator (MLR) to estimate parameters for non-normally distributed data and derived the explicit expression of latent variables with intercepts. A multiple regression prediction model was built to calculate an overall condition index utilizing those measured distress data. The established pavement distress index prediction model provided a rational estimation of weighting coefficients for each distress type. The prediction model showed that alligator cracking, longitudinal cracking in wheel path, non-wheel path longitudinal cracking, transverse cracking, block cracking, edge cracking, patch and bleeding were significant for the latent pavement distress index. |
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| Keywords: | Structural Equation Modeling (SEM) Latent variable Endogenous and exogenous variables Pavement distress condition index Long-term Pavement Performance (LTPP) Multiple regression model |
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