Assessment of Deterioration of Highway Pavement using Bayesian Survival Model

Abstract

The size and level of complexity of highway pavement data and its associated covariates have led to the application of different approaches in the analysis of the highway pavement data for deterioration modeling. With the goal of predicting the survival of highway pavement with interpretable and reproducible models that are robust to uncertainties, errors, and overfitting, the Bayesian survival model (BSM) is proposed in this paper as a good method of estimating parameters for survival functions. Deterioration patterns in relation to the failure time distribution are treated as random quantities sampled from some stochastic prior processes. The specified priors are combined with the data sampled to obtain the distribution of the survival function using Bayes theorem and the Markov chain Monte Carlo method. A posteriori distribution of the survival function is obtained from the pavement information and compared with the classical product limit survival (Kaplan-Meier) estimate and the univariate parametric survival function. This paper reports experimental results of the three candidate models and their efficiency in describing the survival of highway pavement in the presence of deterioration. It is observed from the BSM outcomes that the posterior estimates are accurate in estimating the survival times of roadway segments at 95% credible interval. The outputs also show the robustness of the BSM in describing the uncertainties associated with the survival of highway pavement compared with the Kaplan-Meier and the univariate parametric survival models in the event of limited data and misspecification of underlying distribution.