Pavement Performance Modeling Using Canadian Strategic Highway Research Program Bayesian Statistical Methodology

Abstract

An application of the Canadian Strategic Highway Research Program (C-SHRP) Bayesian statistical analysis methodology for the development of a pavement deterioration model for asphalt concrete surfaces containing steel slag aggregates is described. The asphalt concrete mixes containing steel slag have been used on major highways in Ontario since late 1970s. In 1992 their use was discontinued because of premature pavement deterioration. The purpose of the model was to facilitate timely scheduling of effective rehabilitation treatments for projects containing steel slag mixes. The Bayesian model combines information derived from field observations of 79 existing projects with information elicited from experts. The resulting model predicts the pavement deterioration in terms of a distress index, which is a function of age, the asphalt content of the mix, and traffic volume. The results suggest that the C-SHRP Bayesian statistical analysis approach is useful in that it provides an independent review and endorsement of prediction models by experts, it can increase the application scope, reliability, and predictive power of the models, and it facilitates the quantification and comparison of the influence and contribution of field data and expert judgment in the modeling process.