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- W4387664153 abstract "To accurately predict the existing tunnel deformation induced by shield undercrossing, a BO-Bi-LSTM deep learning network model is developed and trained using shield construction parameters, geological condition parameters, tunnel design parameters and existing tunnel deformation data. By using Bayesian Optimization (BO) algorithm to optimize hyperparameters of Bi-directional Long Short-Term Memory (Bi-LSTM), a prediction model of existing tunnel deformation induced by shield undercrossing based on BO-Bi-LSTM is established and compared with other neural network models. The main findings of this study are as follows: (1) The prediction models established for horizontal and vertical displacement induced by shield undercrossing have high accuracy, and the test sets R2 are 0.935 and 0.924, RMSE are 0.504 and 0.903, and MAE are 0.415 and 0.824, respectively. (2) Compared with Bi-LSTM and GRU models, the proposed existing tunnel deformation prediction model has the highest accuracy and accuracy, and realizes real-time and reliable prediction of tunnel deformation." @default.
- W4387664153 created "2023-10-17" @default.
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- W4387664153 date "2023-10-17" @default.
- W4387664153 modified "2023-10-17" @default.
- W4387664153 title "Prediction of existing tunnel deformation induced by shield undercrossing based on BO-Bi-LSTM" @default.
- W4387664153 doi "https://doi.org/10.1117/12.3009561" @default.
- W4387664153 hasPublicationYear "2023" @default.
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