{"title":"预测波特兰水泥混凝土路面沥青混凝土覆盖层国际粗糙度指数的机器学习模型","authors":"K. Kwon, Y. Yeom, Y. J. Shin, A. Bae, H. Choi","doi":"10.1111/mice.13524","DOIUrl":null,"url":null,"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.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"9 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning models for predicting the International Roughness Index of asphalt concrete overlays on Portland cement concrete pavements\",\"authors\":\"K. Kwon, Y. Yeom, Y. J. Shin, A. Bae, H. Choi\",\"doi\":\"10.1111/mice.13524\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":156,\"journal\":{\"name\":\"Computer-Aided Civil and Infrastructure Engineering\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2025-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer-Aided Civil and Infrastructure Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1111/mice.13524\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13524","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Machine learning models for predicting the International Roughness Index of asphalt concrete overlays on Portland cement concrete pavements
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.
期刊介绍:
Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms.
Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.