Yuan Cao , Shifan Li , Geoffrey Qiping Shen , Hongyu Chen , Yang Liu
{"title":"利用混合算法和数字孪生平台对盾构参数进行智能动态控制","authors":"Yuan Cao , Shifan Li , Geoffrey Qiping Shen , Hongyu Chen , Yang Liu","doi":"10.1016/j.autcon.2024.105882","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a digital twin (DT) platform integrated with an online optimization algorithm that combines Bayesian Optimization (BO), Categorical Boosting (CatBoost), and the Nondominated Sorting Genetic Algorithm (NSGA)-III. The platform enables multi-objective dynamic optimization of shield parameters under varying geological conditions. Using the Wuhan Metro as a case study, the effectiveness of the method is validated. The results demonstrate that: (1) the DT model accurately estimates shield machine performance, with an R<sup>2</sup> of no less than 0.957 on the test set across three geological conditions; (2) the online optimization significantly enhances shield machine performance, with a comprehensive optimization improvement of over 25 % across all conditions; (3) comparison of the constructed algorithm's accuracy, along with Shapley additive explanations, confirms the accuracy, interpretability, and universality of the proposed algorithm.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"169 ","pages":"Article 105882"},"PeriodicalIF":9.6000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intelligent dynamic control of shield parameters using a hybrid algorithm and digital twin platform\",\"authors\":\"Yuan Cao , Shifan Li , Geoffrey Qiping Shen , Hongyu Chen , Yang Liu\",\"doi\":\"10.1016/j.autcon.2024.105882\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents a digital twin (DT) platform integrated with an online optimization algorithm that combines Bayesian Optimization (BO), Categorical Boosting (CatBoost), and the Nondominated Sorting Genetic Algorithm (NSGA)-III. The platform enables multi-objective dynamic optimization of shield parameters under varying geological conditions. Using the Wuhan Metro as a case study, the effectiveness of the method is validated. The results demonstrate that: (1) the DT model accurately estimates shield machine performance, with an R<sup>2</sup> of no less than 0.957 on the test set across three geological conditions; (2) the online optimization significantly enhances shield machine performance, with a comprehensive optimization improvement of over 25 % across all conditions; (3) comparison of the constructed algorithm's accuracy, along with Shapley additive explanations, confirms the accuracy, interpretability, and universality of the proposed algorithm.</div></div>\",\"PeriodicalId\":8660,\"journal\":{\"name\":\"Automation in Construction\",\"volume\":\"169 \",\"pages\":\"Article 105882\"},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-11-22\",\"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/S0926580524006186\",\"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/S0926580524006186","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}
Intelligent dynamic control of shield parameters using a hybrid algorithm and digital twin platform
This paper presents a digital twin (DT) platform integrated with an online optimization algorithm that combines Bayesian Optimization (BO), Categorical Boosting (CatBoost), and the Nondominated Sorting Genetic Algorithm (NSGA)-III. The platform enables multi-objective dynamic optimization of shield parameters under varying geological conditions. Using the Wuhan Metro as a case study, the effectiveness of the method is validated. The results demonstrate that: (1) the DT model accurately estimates shield machine performance, with an R2 of no less than 0.957 on the test set across three geological conditions; (2) the online optimization significantly enhances shield machine performance, with a comprehensive optimization improvement of over 25 % across all conditions; (3) comparison of the constructed algorithm's accuracy, along with Shapley additive explanations, confirms the accuracy, interpretability, and universality of the proposed algorithm.
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.