Toward Monte Carlo Simulation-Based Mechanistic-Empirical Prediction of Asphalt Pavement Performance

Abstract

The mechanistic-empirical study of pavement performance requires that immediate pavement responses due to tire loading be mechanistically computed for pavement structures, and the long-term pavement performance be related to the computed pavement responses. The problem becomes very complicated when variability is considered for loading, pavement and environmental conditions. A Monte Carlo simulation-based mechanistic-empirical pavement design/analysis procedure was verified in this study. The complex tire-pavement interaction was more realistically handled and computed using finite element models and measured tire-pavement contact stress data. The computation time problem involved in pavement response computations was resolved by using a computationally efficient method that relates critical pavement responses to tire loading and pavement structural conditions. In the Monte Carlo simulation, different truck classes were drawn from the truck population empirically based on actual traffic volume data. Axle load spectra were characterized by actual axle load data collected at a weigh-in-motion site. Results from a survey of truck configurations were used to describe the distribution of truck tire pressure. Pavement structural parameters and relationships between material moduli and environmental conditions were obtained from the Long-Term Pavement Performance data. The distress models developed in National Cooperative Highway Research Program Project 1–37A were employed to predict pavement performance. The simulation study estimates the effects of increased tire pressure and steering axle load on a typical pavement structure for 2 million truck passes. The Monte Carlo simulation method and models used in this study can provide useful experience for the development of future flexible pavement design and analysis procedures.