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Unit influence surface identification of long-span bridge based on spatial-temporal vehicle load monitoring

 Unit influence surface identification of long-span bridge based on spatial-temporal vehicle load monitoring
Author(s): , , ,
Presented at IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022, published in , pp. 1295-1303
DOI: 10.2749/nanjing.2022.1295
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On-side bridge unit influence surface (UIS) calibration traditionally relied on the vehicle load test, which is expensive, time-consuming and traffic-interruptive, especially for long-span bridges....
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Bibliographic Details

Author(s): (College of Civil Engineering, Tongji University, Shanghai, China)
(College of Civil Engineering, Tongji University, Shanghai, China)
(College of Civil Engineering, Tongji University, Shanghai, China)
(College of Electronic and Information Engineering, Tongji University, Shanghai, China)
Medium: conference paper
Language(s): English
Conference: IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022
Published in:
Page(s): 1295-1303 Total no. of pages: 9
Page(s): 1295-1303
Total no. of pages: 9
DOI: 10.2749/nanjing.2022.1295
Abstract:

On-side bridge unit influence surface (UIS) calibration traditionally relied on the vehicle load test, which is expensive, time-consuming and traffic-interruptive, especially for long-span bridges. This paper proposes a novel method for bridge UIS identification based on the vehicle load monitoring. By employing a multi-vision system and computer vision algorithms, the distribution of the vehicles on the bridge deck is obtained. Then the data fusion between the vision system and weigh-in-motion (WIM) system is implemented to acquire the spatial-temporal vehicle loads on the deck. In the meanwhile, the deflection of the main-span is also obtained by the SHM system of the bridge. Thus, by means of the iterative computation and surface fitting, the UIS of the deflection is identified. The proposed method is arranged and applied to a practical long-span suspension bridge. Results have shown the feasibility of the method.

Keywords:
computer vision data fusion long-span bridge vehicle load monitoring unit influence surface
Copyright: © 2022 International Association for Bridge and Structural Engineering (IABSE)
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