Performance Prediction Model for Jointed Concrete Pavements in Mississippi Using Machine Learning

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Performance Prediction Model for Jointed Concrete Pavements in Mississippi Using Machine Learning

Rulian, B. / Hakan, Y. / Salma, S. / Yacoub, N.

Pavement performance prediction models are vital to transportation agencies providing decision support for their overall maintenance and rehabilitation (M&R) interventions. Future pavement conditions and rehabilitation priorities have led agencies to the need for developing accurate pavement performance models due to budget allocation issues. The Mississippi Department of Transportation (MDOT) utilizes probability models, which do not include some of the essential parameters such as pavement design, rehabilitation interventions, and traffic. In this study, jointed concrete pavement (JCP) performance models were developed for Mississippi road networks using the artificial neural networks (ANNs) approach to predict the future condition of pavement sections with the effect of M&R actions. A total of 101 JCP sections with 909 data points were used for model development. The developed models successfully characterized the pavement deterioration behavior with a significant agreement between observed and predicted values. Therefore, MDOT can utilize the developed models as a tool for predicting future conditions of JCPs and incorporate the M&R scheduling effectively to prioritize the resources.