Predicting the Thickness of an Excavation Damaged Zone around the Roadway Using the DA-RF Hybrid Model  

作　　者：Yuxin Chen Weixun Yong Chuanqi Li Jian Zhou 

机构地区：[1]School of Resources and Safety Engineering,Central South University,Changsha,410083,China [2]Laboratory 3SR,CNRS UMR 5521,Grenoble Alpes University,Grenoble,38000,France

出　　处：《Computer Modeling in Engineering & Sciences》2023年第9期2507-2526,共20页工程与科学中的计算机建模（英文）

基　　金：funded by the National Science Foundation of China(42177164);the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073);the Innovation-Driven Project of Central South University(2020CX040).

摘　　要：After the excavation of the roadway,the original stress balance is destroyed,resulting in the redistribution of stress and the formation of an excavation damaged zone(EDZ)around the roadway.The thickness of EDZ is the key basis for roadway stability discrimination and support structure design,and it is of great engineering significance to accurately predict the thickness of EDZ.Considering the advantages of machine learning(ML)in dealing with high-dimensional,nonlinear problems,a hybrid prediction model based on the random forest(RF)algorithm is developed in this paper.The model used the dragonfly algorithm(DA)to optimize two hyperparameters in RF,namely mtry and ntree,and used mean absolute error(MAE),rootmean square error(RMSE),determination coefficient(R^(2)),and variance accounted for(VAF)to evaluatemodel prediction performance.A database containing 217 sets of data was collected,with embedding depth(ED),drift span(DS),surrounding rock mass strength(RMS),joint index(JI)as input variables,and the excavation damaged zone thickness(EDZT)as output variable.In addition,four classic models,back propagation neural network(BPNN),extreme learning machine(ELM),radial basis function network(RBF),and RF were compared with the DA-RF model.The results showed that the DARF mold had the best prediction performance(training set:MAE=0.1036,RMSE=0.1514,R^(2)=0.9577,VAF=94.2645;test set:MAE=0.1115,RMSE=0.1417,R^(2)=0.9423,VAF=94.0836).The results of the sensitivity analysis showed that the relative importance of each input variable was DS,ED,RMS,and JI from low to high.

关 键 词：Excavation damaged zone random forest dragonfly algorithm predictive model metaheuristic optimization 

分 类 号：TD3[矿业工程—矿井建设]