Block Collapse Prediction and Reinforcement Optimization in Tunnels Based on Discontinuous Deformation Analysis and Machine Learning Models

Block collapse is one of the most common geological hazards encountered during tunnel construction, characterized by its sudden occurrence and severe consequences. Currently, the prediction and prevention of tunnel block collapse rely primarily on theoretical analysis, numerical simulations, and physical experiments. However, these approaches often oversimplify real‐world conditions and lack efficiency. This study proposes a prediction and reinforcement optimization method for block collapse by integrating discontinuous deformation analysis (DDA) with machine learning method. First, DDA method was employed to simulate tunnel block collapse under structural plane inclination angles of 15°, 30°, and 45°. The corresponding simulation errors compared to model test results were 2.68%, 3.76%, and 1.01%, respectively, demonstrating the accuracy of the DDA approach in modeling block collapse. Next, a dataset of 142 block collapse scenarios under varying conditions was established, encompassing multiple parameters. Among them, the spacing, inclination angle, and internal friction angle of structural planes were identified as having the most significant influence on collapse behavior. Subsequently, five machine learning models were developed to predict collapse height, affected area, and perimeter deformation. All models achieved high coefficients of determination ( R 2 ), with XGBoost exhibiting the best performance. Finally, a data‐driven method for optimizing reinforcement parameters was introduced by integrating the DDA and XGBoost models. Based on prediction results for Cases 128–142, the number of required rock bolts was successfully reduced from 255 to 98 using the proposed optimization strategy. This research provides a valuable reference for the design of reinforcement measures in practical tunnel construction in rock masses.