Inverse Estimation of Influence Line Using Regular Traffic Vehicles for Bridge Weigh-in-Motion
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Bibliographic Details
| Author(s): |
Junki Mori
(University of Yamanashi, Graduate School of Engineering, Yamanashi, Japan)
Junji Yoshida (University of Yamanashi, Graduate School of Engineering, Yamanashi, Japan) Koichi Takeya (Tokyo Institute of Technology, Graduate School of Engineering, Tokyo, Japan) |
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| Medium: | conference paper | ||||
| Language(s): | English | ||||
| Conference: | IABSE Conference: Risk Intelligence of Infrastructures, Seoul, South Korea, 9-10 November 2020 | ||||
| Published in: | IABSE Conference Seoul 2020 | ||||
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| Page(s): | 281-288 | ||||
| Total no. of pages: | 8 | ||||
| DOI: | 10.2749/seoul.2020.281 | ||||
| Abstract: |
Investigating traffic loads and the number of vehicles on bridges is essential in order to grasp factors of deterioration in road bridges. Bridge Weigh-in-Motion (B-WIM) is a method for estimating vehicle axle weight from the response of vehicles passing through a bridge. In this study, we construct a new B-WIM, in which vehicles are tracked from video images and influence line of the bridge is estimated from the response by local buses. As a method of tracking vehicles from video images, we applied Faster Regions with Convolutional Neural Network (Faster R-CNN), which is a method of image processing using deep learning. In addition, influence lines are inversely estimated by the direct search method using deflection responses by local buses. Consequently, the proposed method could estimate axle weights of a large vehicle with over 95 % accuracy. |
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| Keywords: |
influence line Kalman filter B-WIM Moving Images Local Bus Faster R-CNN
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