Deep Learning Method on Deformation Prediction for Large-Section Tunnels

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With the continuous development of engineering construction in China, more and more large-section highway tunnels have emerged. Different geological engineering environments determine the diversity of construction plans. The determination of construction plans and the prediction of tunnel deformations have always been the key points of engineering construction. In this paper, we use numerical simulations to determine specific construction parameters in the context of actual highway tunnel projects, and then use deep learning methods to predict deformation during tunnel construction, thus providing guidance for construction. We have found that: (i) Different excavation sequences and excavation depths have different effects on the surrounding rock deformation around the tunnel. The optimal excavation sequence through numerical simulation in this study is symmetrical excavation, and the excavation depth is 2 m. (ii) Numerical simulation based on Long Short-Term Memory (LSTM) algorithm is used to predict the tunnel deformation. It is found that the prediction results of the LSTM algorithm are more consistent with the actual monitoring data. (iii) Multi-step prediction is more important for engineering guidance, and three-step prediction can be considered during the process of engineering construction. Therefore, the machine learning algorithm provides a new method for engineering prediction.