首页|Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

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Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO)),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters includ-ing pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models'performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSA-SVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.

Underground pillar stabilityHard rockSupport vector machineMetaheuristic algorithms

Chuanqi Li、Jian Zhou、Kun Du、Daniel Dias

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School of Resources and Safety Engineering,Central South University,Changsha 410083,China

Laboratory 3SR,CNRS UMR 5521,Grenoble Alpes University,Grenoble 38000,France

National Natural Science Foundation Project of ChinaNational Natural Science Foundation Project of ChinaDistinguished Youth Science Foundation of Hunan Province of China

7208810142177164202106370038

2023

10.1016/j.ijmst.2023.06.001

矿业科学技术学报(英文版)
中国矿业大学

矿业科学技术学报(英文版)

CSTPCDCSCD北大核心EI
影响因子：1.222
ISSN：2095-2686
年,卷(期)：2023.33(8)
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