水利水电技术（中英文）2025，Vol.56Issue(4)：82-93,12.DOI:10.13928/j.cnki.wrahe.2025.04.007
基于可解释GWO-XGBoost的隧道挤压预测研究
Tunnel squeezing prediction using explainable GWO-XGBoost model
摘要
Abstract
[Objective]To achieve accurate prediction of tunnel squeezing,[Methods]an eXtreme Gradient Boosting(XGBoost)model tuned by Grey Wolf Optimization(GWO)was constructed for tunnel squeezing prediction.Training and testing of the GWO-XGBoost model were conducted on an imbalanced dataset with missing data that had undergone imputation and oversampling techniques.The input features of the GWO-XGBoost model included tunnel burial depth(H),rock tunnelling quality index(Q),diameter(D),strength stress ratio(SSR),and support stiffness(K).The performance of the GWO-XGBoost model was rigorously evaluated using a suite of metrics,including accuracy(ACC),the F1 score,the Kappa coefficient,and the Matthews correlation coefficient(MCC).[Results]The result indicated that the presented GWO-XGBoost model achieved an impressive prediction accuracy of 98.94％on both the training set and the test set.Moreover,on the test set,the cumulative value of the evaluation metrics soared to 5.913 1,underscoring the model's exceptional predictive capabilities.The average Shapley Additive exPlanation(SHAP)values for SSR,D,K,Q,and H were 3.06,1.07,0.82,0.73,and 0.51,respectively,indicating that SSR was the most influential feature affecting the model's output result.[Conclusion]The application of the GWO-XGBoost model to the Huzhubeishan Tunnel and Muzhailing Tunnel has yielded squeezing predictions that closely align with the actual conditions observed,proving the high applicability and predictive accuracy of the presented model in tunnel engineering.关键词
隧道挤压预测/XGBoost/灰狼优化算法/模型解释/缺失数据集/变形/影响因素Key words
tunnel squeezing prediction/XGBoost/grey wolf optimizer/model explanation/missing dataset/deformation/influencing factors分类
交通工程引用本文复制引用
李占科,许正魁,王艳宁,王昆,贾运甫,车璇,关鹏..基于可解释GWO-XGBoost的隧道挤压预测研究[J].水利水电技术（中英文）,2025,56(4):82-93,12.基金项目
国家自然科学基金青年科学基金项目(52008383) （52008383）
湖北省自然科学基金项目(2023AFB369) （2023AFB369）
