—Collection and analysis of pavement distress data is a significant component for effective long-term pavement performance. Accurate, consistent, and repeatable pavement distress type’s evaluation can reduce a tremendous amount of time and money that has been spending each year on maintenance and rehabilitation of existing pavement distress. The main objective of this study is to identify and quantify of surface distress in a given segment of pavement, to perform details distress rating, to predict pavement temperature and cost analysis of individual pavement distress on heavily urban roads in Western Australia (WA). Field survey were conducted from three regions in WA and two approached were used to evaluate and analysis the pavement distress. First, the probabilistic network Marov-Chain Process method was used to predict the cost analysis for individual asphalt concrete surfaced pavement distress. Second, Statistical Downscaling Model (SDSM) was used to predict pavement temperature for asphalt concrete surface pavement. Meteorological data were collected from Perth, Kalgoorlie, and Albany region in WA, and data were used to develop and validation of the model. Different types of pavement distress level were identified and color photograph illustrated the asphalt concrete surfaced pavement. Results were performed and analysis. Results from this study will be useful resource to Main Roads Western Australia, Western Australia State Highways (WASH), and other pavement related users including to the National Highway System (NHS). In addition, results can be used for pavement management systems (PMSs) purpose.
—Pavement distress, crack identification, cost analysis, pavement temperature, pavement management, Western Australia.
The authors are with the Department of Civil Engineering, Curtin
University, GPO Box U1987, Perth, WA 6845, Australia (e-mail:
Cite: Ainalem Nega, Hamid Nikraz, Sujeewa Herath, and Behzad Ghadimi, "Distress Identification, Cost Analysis and Pavement Temperature Prediction for the Long-Term Pavement Performance for Western Australia," International Journal of Engineering and Technology vol. 7, no. 4, pp. 267-275, 2015.