Identifcation of large-scale goaf instability in underground mine using particle swarm optimization and support vector machine

查看全文 作　　者：Zhou [1,2,3]Jian;Li [1,2]Xibing;Hani [3]S.Mitri;Wang [1,2]Shiming;Wei [1,2]Wei

机构地区：[1]School of Resources and Safety Engineering,Central South University;[2]Hunan Key Lab of Resources Exploitation and Hazard Control for Deep Metal Mines;[3]Department of Mining and Materials Engineering,McGill University

出　　处：《International Journal of Mining Science and Technology》索引2013年第23卷第5期,共7页

基　　金：supported by the National Basic Research Program Project of China(No.2010CB732004);the National Natural Science Foundation Project of China(Nos.50934006 and41272304);the Graduated Students’Research;Innovation Fund Project of Hunan Province of China(No.CX2011B119);the Scholarship Award for Excellent Doctoral Student of Ministry of Education of China and the Valuable Equipment Open Sharing Fund of Central South University(No.1343-76140000022) 摘　　要：An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research. 关键词：GOAF Risk identifcation Underground mine Prediction Particle swarm optimization Support vector machine

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Identifcation of large-scale goaf instability in underground mine using particle swarm optimization and support vector machine

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