Estimation of Pavement Performance Deterioration Using Bayesian Approach

Abstract

This paper investigates an incremental pavement performance model based on experimental data from the American Association of State Highway Officials road test. Structural properties, environmental effects, and traffic loading, the three main factors dominating the characteristic of pavement performance, are incorporated into the model. Due to the limited number of variables that can be controlled and observed, unobserved heterogeneity is almost inevitable. Most of the existing models did not fully account for the heterogeneity issue. In this paper, the Bayesian approach is adopted for its ability to address the issue of interest. The Bayesian approach aims to obtain probabilistic parameter distributions through a combination of existing knowledge (prior) and information from the data collected. The Markov chain Monte Carlo simulation is applied to estimate parameter distributions. Due to significant variability in the parameters, the need exists to address heterogeneity in modeling pavement performance. Furthermore, it is shown that not all the parameters are normally distributed. It is therefore suggested that the performance model developed in this research provides a more realistic forecast than most previous models. In addition, pavement deterioration forecast based on the Gibbs output is performed at different confidence levels with varying inspection frequencies, which can enhance the decision-making process in pavement management. In general, the Bayesian approach presented in this paper provides an effective and flexible alternative for model estimation and updating, which can be applied to both the road test data sites and other data sources of interest.