Methodology for Random Surface-Initiated Crack Growth Prediction in Asphalt Pavements

Abstract

Pavement cracking can be considered to be an irreversible step-by-step fracturing process induced by cyclic truck load applications. Hence, pavement crack propagation depends primarily on the accumulated axle loads (ESALs) and also on asphalt mixture stiffness and fatigue characteristics as well as pavement support conditions. This paper presents a model which can predict the distribution of longitudinal surface initiated wheel path crack depths in a family of in-service pavements based on cumulative ESALs. To formulate the primary model, the crack depth versus cumulative ESAL relationship throughout any construction cycle of a given pavement is analytically represented. Pavement failure is tracked by terminal crack indices on record in the Florida Dept. of Transportation’s pavement management database. The random variation of ESALs needed for any given pavement family to reach a specified crack depth is determined based on the variation of life spans of its members and the above analytical relationship. Then, a Markov model is utilized to predict the probability distribution of crack depths at any given cumulative ESAL count. The transitional probabilities associated with specific crack stages are evaluated from the above determined ESAL statistics. A stochastic relationship is also developed between the crack width/depth ratio and cumulative ESALs based on measurements obtained from a large number of core samples. The secondary model can be used to further refine the initial crack depth predictions by utilizing crack width measurements and Bayesian estimation. Model predictions are validated using measured data from field core samples. Finally, it is illustrated how the above models can be used to improve estimation of pavement milling depths prior to rehabilitation.