Application of Machine Learning Techniques for Asphalt Pavement Performance Prediction

Abstract

Pavement management systems (PMS) and maintaining the quality of pavement roads are crucial to human and societal well-being. However, maintaining asphalt pavement quality is complex due to various factors, such as climate change, traffic volume, material properties, and pavement age. This research aims to develop pavement condition index (PCI) models in three U.S. states (California, Hawaii, and New Mexico) using Multiple Linear Regression (MLR) and compared with four additional machine learning (ML) algorithms which are: Random Forest (R.F.), Decision Tree (D.T.), Gradient Boosting (B.G.), and Adaboost were trained. The data obtained was employed for predicting the PCI model as a function of pavement distress and traffic volume. The inputs related to pavement distress and traffic volume variables' effects: pavement age, fatigue cracking, longitudinal cracking, transverse cracking, Cumulative Equivalent Single Axle Load (ESAL), Annual Average Daily Truck Traffic (AADTT), and Annual Average Daily Traffic (AADT). According to the statistical evaluation results, all the ML models exhibited excellent prediction capabilities, as evidenced by their high coefficient of determination (R^2) values of 96.8%,96.6%,97.1%, and 97.4% and low Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Square Error values of 1.888%, 1.874%,1.830, and 1.556%, and 2.529%,2.613%,2.391%, and 2.545% and 6.348%,6.828%,5.716%, and 5.081% and 9.98%, respectively. Furthermore, the results indicate that the ML models demonstrated superior prediction accuracy compared to the (MLR) models developed under the same data.