Prediction Model and Application of Rock Burst Tendency in Deep High Stress Areas

To ensure the construction safety of geotechnical engineering in deep high stress areas, a combined rock burst intensity prediction model based on Whale Optimization Algorithm (WOA) and Extreme Gradient Boosting Tree (XGBoost) is proposed to address the suddenness and complexity of rock burst. Firstly, the main controlling factors that affect the intensity level of rock burst are analyzed, and the uniaxial compressive strength, maximum tangential stress, uniaxial tensile strength, brittleness coefficient, stress coefficient, and elastic energy index are selected to establish a prediction index system for rock burst intensity level. The original samples are processed using the Pearson correlation coefficient, multiple Imputation by Chained Equations (MICE), synthetic minority oversampling technique (SMOTE), and principal component analysis (PCA). Secondly, the maximum number of iterations, maximum depth of the tree, and learning rate of the XGBoost model were optimized through WOA, and the prediction results of the model were comprehensively evaluated using accuracy, precision, recall, F1 score, and Cohen Kappa coefficient. Finally, the model was applied to predict the rock burst intensity level of the Qinlingzhongnanshan highway Tunnel and the water diversion system for hydropower stations. Results show that the WOA-optimized XGBoost model achieves optimal performance when the maximum number of iterations, maximum tree depth, and learning rate are 51, 13, and 0.7325, respectively. Prediction results for rock burst intensity level using the WOA-XGBoost model outperform those of other intelligent algorithm models, verifying the model's high accuracy and reliability in predicting rock burst intensity level.