Machine learning models for predicting the International Roughness Index of asphalt concrete overlays on Portland cement concrete pavements

Abstract

Although estimating the International Roughness Index (IRI) is crucial, previous studies have faced challenges in addressing IRI prediction for asphalt concrete (AC) overlays on Portland cement concrete (PCC) pavements. This study introduces machine learning to predict the IRI of AC overlays on PCC pavements, focusing on incorporating pre‐overlay treatments to reflect their composite characteristics. These treatments are categorized into concrete pavement restoration (CPR) and fracturing methods. The developed models outperformed conventional approaches by effectively capturing the impact of these pre‐overlay treatments, as evidenced by the distinct differences in their contributions to IRI predictions between the CPR and fracturing methods. Additionally, the types and occurrences of pavement distresses varied depending on the pre‐overlay treatments applied. When separate IRI prediction models were developed for each treatment group, they demonstrated improved performance, compared to the original model that combined all treatments. This demonstrates the significance of individualized modeling based on specific pre‐overlay treatment types.