Prediction of existing tunnel deformation induced by shield undercrossing based on BO-Bi-LSTM

Jun Liu; Hongyu Chen; Tiejun Li; Yawei Qin

doi:10.1117/12.3009561

16 October 2023 Prediction of existing tunnel deformation induced by shield undercrossing based on BO-Bi-LSTM

Jun Liu, Hongyu Chen, Tiejun Li, Yawei Qin

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 1280304 (2023) https://doi.org/10.1117/12.3009561
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Liu, Hongyu Chen, Tiejun Li, and Yawei Qin "Prediction of existing tunnel deformation induced by shield undercrossing based on BO-Bi-LSTM", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 1280304 (16 October 2023); https://doi.org/10.1117/12.3009561

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