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An Example of Digital Twins for Bridge Monitoring and Maintenance: Preliminary Results
In this study, we propose a digital twin pilot study for bridge monitoring and maintenance. In particular, an infrastructure management framework using UAV and surveillance cameras, and accelerometers-based digital twins is proposed to perform long-term and non-interruptive monitoring. Real-world monitoring data are obtained through an experimental test performed on the Juanhu bridge (Haning, Zhejiang, China). Traffic flow and accelerometer data of the tested bridge were measured. The digital twin model of the bridge is created as a real-time Finite Element model in OpenSees. The FE model geometry is produced using a 3D photogrammetric reconstruction, and its dynamic properties are updated based on Bayesian modal identification. The traffic flow information on the bridge is processed through computer vision techniques using the video footage from the UAV and surveillance cameras. The object detection algorithm YOLO and tracking algorithm DeepSORT are used to derive the time-space diagrams. These elements operate in tandem with the accelerometer data and the digital twin FE model to acquire a preliminary vehicle loading estimation. The results are presented in this study and showcase the feasibility of the proposed digital twin framework for bridge monitoring and maintenance.
An Example of Digital Twins for Bridge Monitoring and Maintenance: Preliminary Results
In this study, we propose a digital twin pilot study for bridge monitoring and maintenance. In particular, an infrastructure management framework using UAV and surveillance cameras, and accelerometers-based digital twins is proposed to perform long-term and non-interruptive monitoring. Real-world monitoring data are obtained through an experimental test performed on the Juanhu bridge (Haning, Zhejiang, China). Traffic flow and accelerometer data of the tested bridge were measured. The digital twin model of the bridge is created as a real-time Finite Element model in OpenSees. The FE model geometry is produced using a 3D photogrammetric reconstruction, and its dynamic properties are updated based on Bayesian modal identification. The traffic flow information on the bridge is processed through computer vision techniques using the video footage from the UAV and surveillance cameras. The object detection algorithm YOLO and tracking algorithm DeepSORT are used to derive the time-space diagrams. These elements operate in tandem with the accelerometer data and the digital twin FE model to acquire a preliminary vehicle loading estimation. The results are presented in this study and showcase the feasibility of the proposed digital twin framework for bridge monitoring and maintenance.
An Example of Digital Twins for Bridge Monitoring and Maintenance: Preliminary Results
Lecture Notes in Civil Engineering
Pellegrino, Carlo (editor) / Faleschini, Flora (editor) / Zanini, Mariano Angelo (editor) / Matos, José C. (editor) / Casas, Joan R. (editor) / Strauss, Alfred (editor) / Zhou, Chenyu (author) / Xiao, Dahai (author) / Hu, Jianghan (author) / Yang, Yuntao (author)
International Conference of the European Association on Quality Control of Bridges and Structures ; 2021 ; Padua, Italy
Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures ; Chapter: 129 ; 1134-1143
2021-12-12
10 pages
Article/Chapter (Book)
Electronic Resource
English
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