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.

中文翻译：

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用于预测波特兰水泥混凝土路面上沥青混凝土覆盖层的国际粗糙度指数的机器学习模型

尽管估计国际粗糙度指数 （IRI） 至关重要，但以前的研究在解决波特兰水泥混凝土 （PCC） 路面上沥青混凝土 （AC） 覆盖层的 IRI 预测方面面临挑战。本研究引入了机器学习来预测 PCC 路面上 AC 覆盖层的 IRI，重点是结合预覆盖处理以反映其复合特性。这些处理分为混凝土路面修复 （CPR） 和压裂方法。开发的模型通过有效地捕捉这些预覆盖处理的影响，优于传统方法，CPR 和压裂方法之间对 IRI 预测的贡献存在明显差异。此外，路面破损的类型和发生率因所应用的预覆盖处理而异。当为每个治疗组开发单独的 IRI 预测模型时，与结合所有治疗的原始模型相比，它们表现出更好的性能。这证明了基于特定覆盖前处理类型的个性化建模的重要性。