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Smart Structures and Systems Volume 32, Number 5, November 2023 , pages 297-308 DOI: https://doi.org/10.12989/sss.2023.32.5.297 |
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Dynamic deflection monitoring method for long-span cable-stayed bridge based on bi-directional long short-term memory neural network |
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Yi-Fan Li, Wen-Yu He, Wei-Xin Ren, Gang Liu and Hai-Peng Sun
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| Abstract | ||
| Dynamic deflection is important for evaluating the performance of a long-span cable-stayed bridge, and its continuous measurement is still cumbersome. This study proposes a dynamic deflection monitoring method for cable-stayed bridge based on Bi-directional Long Short-term Memory (BiLSTM) neural network taking advantages of the characteristics of spatial variation of cable acceleration response (CAR) and main girder deflection response (MGDR). Firstly, the relationship between the spatial and temporal variation of the CAR and the MGDR is described based on the geometric deformation of the bridge. Then a data-driven relational model based on BiLSTM neural network is established using CAR and MGDR data, and it is further used to monitor the MGDR via measuring the CAR. Finally, numerical simulations and field test are conducted to verify the proposed method. The root mean squared error (RMSE) of the numerical simulations are less than 4 while the RMSE of the field test is 1.5782, which indicate that it provides a cost-effective and convenient method for real-time deflection monitoring of cable-stayed bridges. | ||
| Key Words | ||
| BiLSTM; cable acceleration; cable-stayed bridge; dynamic deflection | ||
| Address | ||
| (1) Yi-Fan Li, Wen-Yu He: Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui 230009, China; (2) Wei-Xin Ren: College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, Guangdong 518061, China; (3) Gang Liu, Hai-Peng Sun: China Design Group, Nanjing, Jiangsu 210000, China. | ||