Songsong Wang, Wenxuan Qiao, Lei Wang, Zhewei Shen, Pengju Yang, Li Bian
{"title":"Research on Substation Engineering Estimates Based on BIM-DE-RF","authors":"Songsong Wang, Wenxuan Qiao, Lei Wang, Zhewei Shen, Pengju Yang, Li Bian","doi":"10.12720/jait.14.5.892-896","DOIUrl":null,"url":null,"abstract":"—Aiming at the problems of heavy workload and large errors in traditional substation engineering estimation methods, an intelligent estimation method for substation engineering based on Building Information Modeling (BIM) combined with a Differential Evolution (DE) algorithm to optimize Random Forest (RF) is proposed. This proposed method uses DE to optimize the RF model’s splitting features and decision trees to enhance the model’s estimation accuracy. The BIM of the substation project is used to determine engineering quantity information, which serves as the input of the DE-RF model, enabling intelligent cost estimation of the substation project. The results of the example analysis show that the relative error of the proposed cost estimation method for substation engineering based on BIM and DE-RF is below 10%. This accuracy level meets various substation engineering cost estimation scenarios, validating the feasibility and correctness of the proposed model.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12720/jait.14.5.892-896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
—Aiming at the problems of heavy workload and large errors in traditional substation engineering estimation methods, an intelligent estimation method for substation engineering based on Building Information Modeling (BIM) combined with a Differential Evolution (DE) algorithm to optimize Random Forest (RF) is proposed. This proposed method uses DE to optimize the RF model’s splitting features and decision trees to enhance the model’s estimation accuracy. The BIM of the substation project is used to determine engineering quantity information, which serves as the input of the DE-RF model, enabling intelligent cost estimation of the substation project. The results of the example analysis show that the relative error of the proposed cost estimation method for substation engineering based on BIM and DE-RF is below 10%. This accuracy level meets various substation engineering cost estimation scenarios, validating the feasibility and correctness of the proposed model.