高压物理学报2026，Vol.40Issue(2)：73-88,16.DOI:10.11858/gywlxb.20251103
深部高应力区岩爆烈度等级预测模型及应用
Prediction Model and Application of Rock Burst Tendency in Deep High Stress Areas
摘要
Abstract
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(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 0.732 5,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.关键词
岩爆/鲸鱼优化算法(WOA)/极端梯度提升树(XGBoost)/链式方程多重插补法(MICE)/合成少数类过采样技术(SMOTE)Key words
rock burst/whale optimization algorithm(WOA)/extreme gradient boosting(XGBoost)/multiple imputation by chained equations(MICE)/synthetic minority oversampling technique(SMOTE)分类
资源环境引用本文复制引用
祁云,白晨浩,段宏飞,代连朋,李绪萍,汪伟..深部高应力区岩爆烈度等级预测模型及应用[J].高压物理学报,2026,40(2):73-88,16.基金项目
国家自然科学基金(52174188,52464020) （52174188,52464020）
内蒙古自治区自然科学基金(2024LHMS05012) （2024LHMS05012）
山西省研究生实践创新项目(2024SJ378) （2024SJ378）
山西大同大学研究生实践创新项目(2024SJCX05) （2024SJCX05）
