Xudong Jian, Jiwei Zhong, Yafei Wang, Ye Xia, Limin Sun
{"title":"Bridge weigh-in-motion using bridge influence surface and computer vision: an experimental study","authors":"Xudong Jian, Jiwei Zhong, Yafei Wang, Ye Xia, Limin Sun","doi":"10.2749/ghent.2021.0435","DOIUrl":null,"url":null,"abstract":"Complicated traffic scenarios, including random lane change and multiple presences of vehicles on bridges are the main obstacles preventing bridge weigh-in-motion (BWIM) technique from reliable and massive application. To tackle the complicated traffic problems of BWIM, this paper develops a novel BWIM method by integrating the bridge influence surface theory and deep-learning based computer vision technique. For illustration and verification, the proposed method is applied to identify gross weights of vehicles in scale experiments, where various complicated traffic scenarios are simulated. Identification results confirm the favourable robustness, accuracy, and cost- effectiveness of the method.","PeriodicalId":162435,"journal":{"name":"IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2749/ghent.2021.0435","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
Complicated traffic scenarios, including random lane change and multiple presences of vehicles on bridges are the main obstacles preventing bridge weigh-in-motion (BWIM) technique from reliable and massive application. To tackle the complicated traffic problems of BWIM, this paper develops a novel BWIM method by integrating the bridge influence surface theory and deep-learning based computer vision technique. For illustration and verification, the proposed method is applied to identify gross weights of vehicles in scale experiments, where various complicated traffic scenarios are simulated. Identification results confirm the favourable robustness, accuracy, and cost- effectiveness of the method.