Risk classification assessment and early warning of large deformation of soft rock in tunnels based on CNN-LSTM model

Abstract

Abstract Tunnels in the Western Plateau region of China often face numerous problems of high ground stress, as well as highly fractured and weak rock masses in deep sections of the tunnel, which results in an increased risk of large soft rock deformation in tunnels, posing substantial challenges to safe and efficient construction. Hence, risk assessment and establishment of an early safety warning system are essential. By examining factors affecting the large soft rock deformation in tunnels, a set of index system consisting of three primary indicators (i.e., hydrogeological conditions, design factors, and construction factors) and eight secondary indicators (i.e., strength-to-stress ratio, rock mass index, groundwater influence, groundwater influence, etc.) for assessing these risks is constructed, while the risk classification and identification criteria are also established. As an example, a tunnel on the Xicheng Railway is adopted for regression prediction of rock mass deformation and risk warning classification based on CNN-LSTM model. The results indicate that the model is effective in predicting rock mass deformation with minimal fluctuations in settlement data, a MAPE of 0.29525, and a RMSE of 6.1311. The model demonstrates a high accuracy rate of 91.67% for early risk warning prediction of soft rock deformation in the tunnel, moreover, when applied to 10 cross-sections of the Xicheng Railway Tunnel for deformation risk early warning, the model demonstrated a high prediction accuracy of 90%. which serves as a reference for the assessment and prediction of large deformation risks in similarly complex and challenging tunnels in the western region of China.

Keywords

• Tunneling
• Large soft rock deformation
• Convolutional neural network (CNN)
• Long short-term memory neural network (LSTM)
• Risk assessment