{"title":"利用 XGBoost 和 NSGA-II 对道路养护进行数据驱动的多目标优化","authors":"Jiale Li, Song Zhang, Xuefei Wang","doi":"10.1016/j.autcon.2024.105750","DOIUrl":null,"url":null,"abstract":"<div><p>Road maintenance is crucial for road comfort. Inappropriate maintenance construction works may cause waste in budget and extra greenhouse gas emissions. Previous studies designed construction plans based on experience and the current distress stage of the road, without considering the cost and carbon emissions between different construction plans throughout the life cycle. The road deterioration tendency, however, is complicated and depends on multiple factors. This paper presents a two-layer multi-objective optimization maintenance decision support system based on 10-year maintenance and inspection historical data. Pareto frontier is used to provide a maintenance construction plan to a hundred-meter interval. A case study demonstrates that this approach can increase road performance by 6.6 %, reduce costs by 69.56 %, and reduce carbon emissions by 88.2 % compared with the practical maintenance plan. This study considered the data-driven deterioration tendency, carbon emission, and cost associated with various construction methods in maintenance strategy formulation.</p></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-driven multi-objective optimization of road maintenance using XGBoost and NSGA-II\",\"authors\":\"Jiale Li, Song Zhang, Xuefei Wang\",\"doi\":\"10.1016/j.autcon.2024.105750\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Road maintenance is crucial for road comfort. Inappropriate maintenance construction works may cause waste in budget and extra greenhouse gas emissions. Previous studies designed construction plans based on experience and the current distress stage of the road, without considering the cost and carbon emissions between different construction plans throughout the life cycle. The road deterioration tendency, however, is complicated and depends on multiple factors. This paper presents a two-layer multi-objective optimization maintenance decision support system based on 10-year maintenance and inspection historical data. Pareto frontier is used to provide a maintenance construction plan to a hundred-meter interval. A case study demonstrates that this approach can increase road performance by 6.6 %, reduce costs by 69.56 %, and reduce carbon emissions by 88.2 % compared with the practical maintenance plan. This study considered the data-driven deterioration tendency, carbon emission, and cost associated with various construction methods in maintenance strategy formulation.</p></div>\",\"PeriodicalId\":8660,\"journal\":{\"name\":\"Automation in Construction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Automation in Construction\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0926580524004862\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524004862","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Data-driven multi-objective optimization of road maintenance using XGBoost and NSGA-II
Road maintenance is crucial for road comfort. Inappropriate maintenance construction works may cause waste in budget and extra greenhouse gas emissions. Previous studies designed construction plans based on experience and the current distress stage of the road, without considering the cost and carbon emissions between different construction plans throughout the life cycle. The road deterioration tendency, however, is complicated and depends on multiple factors. This paper presents a two-layer multi-objective optimization maintenance decision support system based on 10-year maintenance and inspection historical data. Pareto frontier is used to provide a maintenance construction plan to a hundred-meter interval. A case study demonstrates that this approach can increase road performance by 6.6 %, reduce costs by 69.56 %, and reduce carbon emissions by 88.2 % compared with the practical maintenance plan. This study considered the data-driven deterioration tendency, carbon emission, and cost associated with various construction methods in maintenance strategy formulation.
期刊介绍：
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
