Abstract
Deformation prediction of extremely high in situ stress in soft-rock tunnels is a complex problem involving many parameters, and traditional analytical solutions and numerical simulations have difficulty achieving satisfactory results. This paper proposes the MIC-LSTM algorithm based on machine learning methods to predict the deformation of soft-rock tunnels under extremely high in situ stress conditions caused by construction. The study first analyzed the difficulties of engineering construction and the construction plan; then, numerical simulation was used to verify the modified construction plan. To prove that the construction plan was reasonable, machine learning was used to analyze the correlation of the various parameters that cause tunnel deformation; then, the future deformation of the tunnel was predicted. The study found that: (1) the new construction scheme contains symmetrical arrangement of bolts and two support structures along the tunnel vault can effectively control the deformation of the tunnel, and meet the requirements of the specification; (2) the rock uniaxial compressive strength had the greatest impact on tunnel deformation, and the rock humidity had the least influence on tunnel deformation; and (3) the prediction curve based on the deep learning model had a higher similarity to the monitoring curve compared with the traditional numerical analysis software. The MIC-LSTM machine algorithm provides a new approach to predicting the deformation of extremely high in situ stress soft-rock tunnels.
Highlights
Tunnel construction is often faced with a series of unfavorable factors, such as extreme cold, high altitude, high in situ stress, etc
Aydan et al [14] proposed a method to predict the deformation of high in situ stress in a soft-rock tunnel by using the tangential relative strain based on the rock constitutive curve obtained under uniaxial compression
Long Short-Term Memory (LSTM) is a special recurrent neural network (RNN) that is mainly used to solve the problem of gradient disappearance and gradient explosion during the process of long sequence training [46-48]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Many scholars have studied deformation prediction and the control technology of high in situ stress soft-rock tunnels [10–30]. Aydan et al [14] proposed a method to predict the deformation of high in situ stress in a soft-rock tunnel by using the tangential relative strain based on the rock constitutive curve obtained under uniaxial compression. Jia et al [19] discussed the effect of prestressed bolts and bolt-cable support technology in controlling the deformation of surrounding high in situ stress soft rock through the finite difference numerical software FLAC3D. This paper proposes that the MIC-LSTM algorithm be used to analyze the correlation of the relevant factors that cause tunnel deformation, and predicts the deformation of the tunnel to provide guidance for construction
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