| Paper ID | ASPS-6.6 |
| Paper Title |
FULLY-NEURAL APPROACH TO VEHICLE WEIGHING AND STRAIN PREDICTION ON BRIDGES USING WIRELESS ACCELEROMETERS |
| Authors |
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi, National Institute of Informatics, Japan; Haoqi Wang, Tomonori Nagayama, University of Tokyo, Japan |
| Session | ASPS-6: Sensing & Sensor Processing |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Applied Signal Processing Systems: Signal Processing Systems [DIS-EMSA] |
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| Virtual Presentation |
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| Abstract |
Bridge weigh-in-motion (BWIM) is a technique of estimating vehicle loads on bridges and can be used to assess a bridge's structural fatigue and therefore its life. BWIM can be realized by analyzing the bridge deflection in terms of its response to moving axle loads. To obtain accurate load estimates, current BWIM systems require strain sensors, whose (re-) installation costs have limited their application. In this paper, we propose a new BWIM approach based on a deep neural network using accelerometers, which are easier to install than strain sensors, thus helping the advancement of low-cost BWIM systems. By learning the bridge dynamism, our model estimates axle loads successfully from the noisy acceleration signals sampled on a real bridge in service. |
