Prediction of service life of asphaltic concrete pavements with surface courses containing steel slag aggregates using Bayesian statistical methodology.

This report describes an application of the C-SHRP Bayesian statistical methodology for the development of a pavement deterioration model for asphaltic concrete surfaces containing steel slag aggregates. The asphaltic concrete mixes containing steel slag have been used in Ontario on major highways since late 70's. 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, asphalt content of the mix, and traffic volume. The results indicate that C-SHRP Bayesian statistical analysis approach is useful in that (a) it provides an independent review and endorsement of prediction models by experts, (b) it can increase the application scope, reliability and predictive power of the models, and (c) it facilitates quantification of the influence and contribution of field data and expert judgement in the modelling process. (A)