Implementation of digital twin and support vector machine in structural health monitoring of bridges
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Budapest University of Technology and Economics, Faculty of Civil Engineering, Muegyetem rkp. 3, 1111 Budapest, Hungary
University of Wolverhampton, Wulfruna St, Wolverhampton WV1 1LY, the United Kingdom
Silesian University of Technology, Faculty of Civil Engineering, ul. Akademicka 2A, 44-100 Gliwice, Poland
Submission date: 2023-01-03
Final revision date: 2023-05-08
Acceptance date: 2023-05-10
Publication date: 2023-09-18
Archives of Civil Engineering 2023;3(3):31-47
Structural health monitoring (SHM) of bridges is constantly upgraded by researchers and bridge engineers as it directly deals with bridge performance and its safety over a certain time period. This article addresses some issues in the traditional SHM systems and the reason for moving towards an automated monitoring system. In order to automate the bridge assessment and monitoring process, a mechanism for the linkage of Digital Twins (DT) and Machine Learning (ML), namely the Support Vector Machine (SVM) algorithm, is discussed in detail. The basis of this mechanism lies in the collection of data from the real bridge using sensors and is providing the basis for the establishment and calibration of the digital twin. Then, data analysis and decision-making processes are to be carried out through regression-based ML algorithms. So, in this study, both ML brain and a DT model are merged to support the decision-making of the bridge management system and predict or even prevent further damage or collapse of the bridge. In this way, the SHM system cannot only be automated but calibrated from time to time to ensure the safety of the bridge against the associated damages.
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