Probabilistic prediction of asphalt pavement performance

Variability of pavement design parameters has always been a concern to pavement designers and highway agencies. A robust pavement design should take into account the variability of the design inputs and its impact on the reliability of the design. In this study, the variability effect of thickness and stiffness of pavement layers was investigated. The variability of these parameters was described by their mean values, standard deviations and probability distribution functions. Monte Carlo Simulation method was utilised to incorporate variability of the design parameters and to construct the probability distribution function of the outputs. KENLAYER software was used to calculate pavement response at predetermined critical locations; pavement reponse was then used to predict pavement performance regarding permanent deformation, bottom-up and top-down fatigue cracking by using the mechanistic empirical pavement design guide (MEPDG) models. A Matlab code was developed to run that analysis and obtain the probability distribution function of pavement performance indicators over time. It was found that the variability of pavement layer thickness and stiffness has a significant impact on pavement performance. Also, it was found that not only the mean of the predicted performance indicators is increasing over time, but the variance of these indicators is also increasing. This means that pavement condition cannot be described by the mean values of the indicators but by the probability distribution function which can describe pavement condition at any reliability level