Framework for PMS Using Mechanistic Distress Submodels

Abstract

A framework for a pavement-management system is presented, which employs mechanistic performance submodels to predict pavement behavior. These performance submodels predict the development of pavement distresses in terms of traffic loading and pavement structure. This approach enables the effectiveness of pavement rehabilitation strategies to be evaluated with respect to individual distress modes. It also forms the basis for a dynamic programming optimization scheme, which considers life-cycle costs for budget allocation. The performance submodels are formulated within a probabilistic framework to incorporate uncertainty information on the factors governing pavement deterioration. The transition probabilities from one pavement state to another for a set of rehabilitation alternatives are determined using the first-order reliability method (FORM). The methodology allows multiple distress mode condition to be considered using the concept of cut sets. These probabilities serve as inputs to the optimization submodel, which determines the optimal rehabilitation strategy in terms of discounted life-cycle costs subjected to management and operational constraints.