Machine Learning-Based Prediction of International Roughness Index for Continuous Reinforced Concrete Pavements

Abstract

The International Roughness Index (IRI) serves as a crucial indicator for ride quality and user comfort. As road roughness escalates, road serviceability diminishes, resulting in reduced vehicle speed and increased travel time, and consequently higher carbon dioxide emissions. Predicting the IRI is of utmost importance for pavement management systems and sustainable development overall. While numerous studies have forecasted the IRI of fl exible pavements, there is a notable scarcity of research focusing on rigid pavement performance prediction. This study addresses the gap in predicting IRI for Continuous Reinforced Concrete Pavements (CRCP), an understudied aspect of pavement engineering. Leveraging the Long-Term Pavement Performance database, different machine learning techniques were applied to different input parameter representations. There are 90 measurements for the data points of the IRI. The input variables include the initial IRI, counts of medium-severity and high-severity transverse cracks, counts of medium-severity and high-severity punchouts, the percentage of pavement surface with patching (ranging from medium to high severity in both fl exible and rigid pavements), pavement age, freezing index, and the percentage of subgrade material passing through the No. 200 US sieve. Through data analysis and machine learning algorithms, an accurate IRI prediction model for CRCP is developed. The results of this study show that the adaptive boosting algorithm model for CRCP yielded very good prediction accuracy ( R 2 ¼ 0.90 and 0.83 for training and testing datasets, respectively) with low bias. The study fi ndings offer valuable insights into CRCP IRI prediction, bene fi ting pavement management and maintenance strategies.