Stability prediction of roadway surrounding rock using INGO-RF  

作　　者：Xinchao Cui Hongfei Duan Wei Wang Yun Qi Kailong Xue Qingjie Qi 

机构地区：[1]School of Coal Engineering,Shanxi Datong University,Datong,037000,China [2]Institute of Mine Safety Technology,China Academy of Safety Science and Technology,Beijing,100012,China [3]College of Mechanical Engineering and Automation,Liaoning University of Technology,Jinzhou,121001,China [4]Research Institute Central Research,Institute of Coal Science,Beijing,100013,China

出　　处：《Geohazard Mechanics》2024年第4期270-278,共9页岩土灾变力学(英文)

基　　金：supported by the National Natural Science Foun-dation of China (No.52174188);the Shanxi Basic Research Program (Free Exploration)Project of China (No.202203021222300).

摘　　要：In order to more accurately classify the stability of roadway surrounding rock and identify dangerous areas in a timely manner to prevent roadway collapse and other disasters,this study proposes an Improved Northern Gok algorithm(INGO)and Random Forest(RF)roadway surrounding rock stability prediction model.This model combines the improved INGO-RF based on the analysis of influencing factors of roadway surrounding rock stability.First,three strategies were employed to enhance the Northern Gob algorithm(NGO):logistic chaotic mapping,refraction reverse learning,and improved sine and cosine.Subsequently,INGO was utilized to optimize the number of decision trees and the minimum number of leaf nodes for RF species in order to improve the prediction accuracy of RF.Secondly,a data set consisting of 34 groups of roadway surrounding rock data is selected.The input indexes of the model include the roof strength,two-wall strength,floor strength,burial depth,roadway pillar width,ratio of direct roof thickness to mining height,and surrounding rock integrity.Meanwhile,surrounding rock stability is considered as the output index.Particle swarm optimization backpropagation neural network(PSO-BPNN),genetic algorithm optimization support vector machine(GA-SVM),Sparrow Search Algorithm optimization RF(SSA-RF)models were introduced to compare the predictive results with the INGO-RF model,and the results showed that:INGO-RF model has the best performance in the comparison of various performance indicators;compared with other models,the accuracy rate(Ac)in the test set has increased by 0.12–0.40,the accuracy rate(Pr)has increased by 0.07–0.65,and the recall rate(Re)has increased by 0.08–0.37;the harmonic mean(F1-Score)of the recall rate increased by 0.08–0.52,the mean absolute error(MAE)decreased by 0.1428–0.4285,the mean absolute percentage error(MAPE)decreased by 7.15%–28.57%,and the root mean square error(RMSE)decreased by 0.1565–0.3779;and finally,the data on surrounding rock conditions of roadways in multiple mining

关 键 词：Roadway surrounding rock stability Northern goshawk optimization(NGO) Random forest(RF) Prediction model Model performance index 

分 类 号：TD3[矿业工程—矿井建设]