A method of modeling temperature-strain mapping relationship for long-span cable-stayed bridges using transfer learning and bi-directional long short-term memory neural network

FANG Jiachang1,HUANG Tianli1,LI Miao2,WANG Yafei3

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (12) : 126-134.

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (12) : 126-134.

A method of modeling temperature-strain mapping relationship for long-span cable-stayed bridges using transfer learning and bi-directional long short-term memory neural network

  • FANG Jiachang1,HUANG Tianli1,LI Miao2,WANG Yafei3
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Abstract

To rapidly construct and accurately predict the strain responses of the main girder induced by temperature in the long-span cable-stayed bridge for structural condition assessment, based on the measured temperature and strain data on the main girder of a long-span cable-stayed bridge over 1 year, a method of constructing the temperature-strain mapping model by using the transfer learning technique and the bidirectional long short-term memory (Bi-LSTM) neural networks is proposed in this study. Firstly, the analytical mode decomposition (AMD) is adopted to denoise the strain data to obtain the temperature-induced strain. Secondly, the temperature and the strain data at a particular measurement point were selected to form a dataset, and were fed to a Bi-LSTM neural network. Then a well-fitting neural network baseline model is constructed by optimizing the network structure and hyperparameters. Finally, using the transfer learning method, some parameters from the trained Bi-LSTM neural network model are transferred to other temperature-strain datasets to construct the transferred temperature-strain mapping models. Compared with the temperature-strain Bi-LSTM neural network models constructed directly from the datasets, the transferred temperature-strain Bi-LSTM neural network models built by using the transfer learning technique have higher fitting accuracy, shorter training time and smaller prediction error.

Key words

structural health monitoring / long-span cable-stayed bridge / temperature-strain mapping model / transfer learning / bi-directional long short-term memory (Bi-LSTM) neural network

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FANG Jiachang1,HUANG Tianli1,LI Miao2,WANG Yafei3. A method of modeling temperature-strain mapping relationship for long-span cable-stayed bridges using transfer learning and bi-directional long short-term memory neural network[J]. Journal of Vibration and Shock, 2023, 42(12): 126-134

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