Development of Pavement Performance Models for MDOT: A Neural Network Approach

The continuous investigation into the mechanical properties and rehabilitation management of existing pavements is vital for the sustainability and efficiency of transportation systems. The Mississippi Department of Transportation (MDOT) database, containing over 40 million records, necessitates modernized decision-making models that align with current design methods and materials. In collaboration with the University of Mississippi, MDOT's pavement management program aimed to update outdated Markov transition matrices with advanced Artificial Neural Networks (ANNs). This four-year project focuses on dynamic sequential ANNs to develop performance prediction models for flexible, rigid, and composite pavements, utilizing extensive distress data. Initial results for flexible pavements showed promising outcomes, with robust statistical accuracy measures for rigid pavements (JCP and CRCP) and composite pavements, although further data and calibration are recommended. The developed models, accessible through a user-friendly interface, are expected to improve the prioritization of maintenance and rehabilitation resources, achieving significant time and cost savings while enhancing the overall efficiency of MDOT's pavement management practices.