Bayesian hierarchical modelling of pavement performance

A challenge currently faced by local, state and federal transportation agencies is the constantly increasing traffic demand, combined with a less increasing availability of funds for the maintenance of the highway infrastructure. A key factor for the success of a pavement management system is that it contains accurate and reliable pavement performance models. Inadequate prediction of the highway infrastructure future condition can lead to an inappropriately estimated budget or misallocation of funds. This study had the main objectives of quantifying the uncertainty of pavement performance model parameters and proposing a hierarchical model specification in order to account for heterogeneity across different subpopulations of pavements. The uncertainty of each pavement performance parameter was quantified by estimating their marginal posterior distribution using both a non-hierarchical and a hierarchical specification of the model. The posterior distribution of each model parameter was sampled using a combination of the Gibbs and Metropolis-Hastings techniques. The hierarchical model was specified in order to capture the different damaging effect that environmental factors and traffic characteristics have on pavements between the subpopulations with thinner and thicker hot-mix asphalt layer. The results from the study showed a significant dispersion of the pavement performance parameters. In addition, accounting for the heterogeneous effect between subpopulations resulted in a significant improvement of the fitting of the model as opposed to assuming complete pooling across pavement sections.