Pavement Deterioration Prediction Model and Project Selection for Kentucky Highways

Abstract

Pavement deterioration is an important factor in evaluating and prioritizing pavement management and preservation (PMP) projects. The primary goal of this paper is to provide quality predictive functions from multiple linear regression (MLR) models that can be adopted by Kentucky Transportation Cabinet (KYTC). Furthermore, the paper proposes to use a decision analysis procedure, i.e., an analytic hierarchy process (AHP), in developing a composite pavement distress index for KYTC to prioritize and select PMP projects. Such a prioritization of candidate PMP projects is based on 11 different distress indices. Numerical results show that the MLR models provide relatively high R squared values of approximately 0.8. In addition, preliminary study shows that the proposed AHP-based project selection method overcomes the drawback of KYTC's current rating and selection system for overemphasizing the international roughness index (IRI) among all distress indices.