Machine learning for predicting pavement roughness and optimising maintenance

This study examines four machine learning algorithms for predicting short-term and long-term International Roughness Index (IRI) changes in pavement performance. Using over 2,700 samples from the Long-Term Pavement Performance database, models are developed to forecast IRI changes under various maintenance scenarios. The approach accounts for immediate IRI changes caused by treatments and includes separate models for predicting weather and traffic variables. Random Forest models demonstrated the best performance, achieving R² values of 80–96% for long-term IRI changes and 85–92% for short-term changes across different maintenance strategies. Subsequently, Support Vector Machine (SVM) models with radial, linear, and polynomial kernels presented R² values as high as 70–84%, 60–92%, and 60–83%, respectively. The methodology enables accurate prediction of pavement roughness, facilitating treatment prioritisation and offering time and cost savings in data collection. It provides a comprehensive schedule for treatment execution, including types, timing, locations, and costs, thereby enhancing pavement management efficiency.