Comparative Evaluation of Machine Learning Models for Pavement Roughness Prediction

This study aims to develop and compare different models for predicting the International Roughness Index (IRI) of 419 long-term pavement performance pavement sections from seven states in the United States. The IRI is a crucial metric used to evaluate pavement roughness, ranging from very smooth to very rough surfaces. Machine learning models have gained popularity in predicting IRI as a result of their ability to analyze large volumes of data, improving accuracy and providing cost-effective solutions for pavement management and maintenance. The developed models are generalized linear model (GLM), support vector machine regression, multivariate adaptive regression splines, artificial neural network (ANN), and extreme gradient boosting (XGBoost). The XGBoost model outperformed the other models with the lowest root mean square error and the highest R 2 . The GLM model showed good performance for lower values of the roughness index while it underpredicted higher values. After regularization and feature selection, the input variables that were common to all models included age, structural number, deflection at 1.5 times of pavement thickness, traffic load, precipitation, and maintenance and rehabilitation history. By utilizing these models, pavement engineers can make informed decisions, allocate resources efficiently, and prioritize maintenance activities based on accurate predictions of pavement roughness.