国家自然科学基金青年科学基金 (52009090)；天津市自然科学基金 (19JCYBJC22600)
(1.State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China;2.Hanjiang-to-Weihe River Valley Water Diversion Project Construction Co., Ltd., Shaanxi Province, Xi’an 710302, China)
In order to realize the effective judgment of surrounding rock classification under the condition of imbalanced surrounding rock classification and improve the simulation accuracy of tunnel construction, a study on the tunnel construction simulation is carried out based on the advanced classification of imbalanced surrounding rock with improved eXtreme Gradient Boosting (XGBoost). The Automatic Neighborhood size Determination-SMOTE (AND-SMOTE) method is used to optimize the class imbalance of surrounding rock, and the improved XGBoost model is used for advanced classification of surrounding rock, and then the simulation parameters are optimized, which improved the accuracy of tunnel construction simulation. In order to improve the accuracy of surrounding rock classification, the Harris Hawks Optimization (HHO) algorithm is used to automatically optimize the hyperparameters of XGBoost ensemble classifier. The engineering application shows that the improved XGBoost model has higher classification accuracy than the unimproved XGBoost, KNN, SVC and other 6 models. The classification accuracy of the improved XGBoost model is improved by 8.6% after considering the class imbalance of surrounding rock. In addition, the relative deviation between the results of tunnel construction simulation based on the advanced classification of surrounding rock and the actual progress is reduced by 11.3% compared with the traditional simulation, which is more in line with the engineering reality.
韩峰,余佳,徐国鑫,等.基于改进XGBoost不平衡围岩超前分类方法的隧洞工程施工仿真研究[J].河海大学学报(自然科学版),2023,51(1):150-157.(HAN Feng, YU Jia, XU Guoxin, et al. Study on tunnel construction simulation based on advanced classification of imbalanced surrounding rock using improved XGBoost[J]. Journal of Hohai University (Natural Sciences),2023,51(1):150-157.(in Chinese))复制