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

To rapidly construct and accurately predict the strain responses of amain girder induced by temperature in along-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 was proposed in this study. Firstly, the analytical mode decomposition (AMD) was 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 was constructed by optimizing the network structure and hyperparameters. Finally, using the transfer learning method, some parameters from the trained Bi-LSTM neural network model were 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. © 2023 Chinese Vibration Engineering Society. All rights reserved.