Stochastic Forecasting of Life Expectancies Considering Multi-Maintenance Criteria and Localized Uncertainties in the Pavement-Deterioration Process

Abstract

Abstract Understanding the deterioration characteristics of highway pavements is an essential factor for developing better maintenance strategies, as well as estimating mid- to long-term budget demands. In general, estimation results regarding life expectancy have often been represented with a constant in deterministic manners. However, in reality, these results do not correspond with real life spans because of the severe uncertainties in the pavement-deterioration process and various maintenance criteria. For these reasons, in this paper, the real life expectancy was assessed in a stochastic manner with the Bayesian Markov hazard model, which is suitable for estimating localized uncertainties in the progress of deterioration based on various deterioration indices. On the basis of this model, we have developed a new methodology that can determine the joint distribution of total maintenance demands by aggregating multiple deterioration processes using the concept of “safety-hazard zones.” In addition, we developed a method for disaggregating the joint distribution by maintenance types to mitigate the conventional problems associated with the probabilistic budget estimation approach. For our empirical study, we used data from the Korean national highways from 2007 to 2011 concerning pavement conditions, including crack, rut depth, and international roughness index (IRI). The results of this study are meaningful in that they establish the ideal conditions required for the Bayesian Markov hazard model to forecast the deterioration of pavement, allowing the incorporation of a practical viewpoint into an academic approach under the concept of asset management.