Hybrid Approach to Incorporate Preventive Maintenance Effectiveness into Probabilistic Pavement Performance Models

Abstract

Various methodologies are being developed to build and improve probabilistic pavement performance models that have high prediction capabilities. However, the effectiveness of preventive maintenance (PM) has not been considered in such models due to the lack of historical PM data. Consequently, the predicted pavement condition is erroneous and often biased, which leads to nonoptimal maintenance and rehabilitation (M&R) decisions. This paper introduces and validates a hybrid approach to incorporate the impact of PM into probabilistic pavement performance models when historical PM data are absent. The types of PM treatments and their times of application are estimated using two approaches: (1) analysis of the state of practice of pavement maintenance through literature and expert surveys, and (2) detection of PM times from probabilistic pavement performance curves. Using a newly developed optimization algorithm, the estimated times and types of PM treatments are integrated into pavement condition data. A nonhomogeneous Markovian pavement performance model is developed by estimating the transition probabilities of pavement condition using the ordered probit method. The developed hybrid approach and performance models are validated using cross validation with out-of-sample data and through surveys of subject matter experts in pavement engineering and management. The results show that the hybrid approach and models developed predict probabilistic pavement condition incorporating PM effects with an accuracy of 87%.