Implementation of digital twin and support vector machine in structural health monitoring of bridges

• Autorzy: Al-Hijazeen Asseel Za'al Ode , Fawad Muhammad , Gerges Michael , Koris Kálmán , Salamak Marek

Identyfikatory

DOI

10.24425/ace.2023.146065

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

structural health monitoring; bridge; damage; digital twin; machine learning; support vector machine

PL

monitorowanie stanu konstrukcji; most; uszkodzenie; bliźniak cyfrowy; uczenie maszynowe; maszyna wektorów wsparcia

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2023
• Tom: Vol. 69, nr 3
• Strony: 31--47
• Opis fizyczny: Bibliogr. 56 poz., il.

Twórcy

autor

Al-Hijazeen Asseel Za'al Ode

• Budapest University of Technology and Economics, Faculty of Civil Engineering, Budapest, Hungary

• https://orcid.org/0000-0001-5835-853X

autor

Fawad Muhammad

• Silesian University of Technology, Faculty of Civil Engineering, Gliwice, Poland
• Budapest University of Technology and Economics, Faculty of Civil Engineering, Budapest, Hungary

• https://orcid.org/0000-0003-4729-7289

autor

Gerges Michael

• University of Wolverhampton, Wulfruna St, Wolverhampton, United Kingdom

• https://orcid.org/0000-0001-5986-7301

autor

Koris Kálmán

• Budapest University of Technology and Economics, Faculty of Civil Engineering, Budapest, Hungary

• https://orcid.org/0000-0002-6571-5555

autor

Salamak Marek

• Silesian University of Technology, Faculty of Civil Engineering, Gliwice, Poland

• https://orcid.org/0000-0003-3602-0575

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