LI Zhang-hong, WANG Yun-fei, CAI Mei-feng, MIAO Sheng-jun, FAN Li-ping. Slope deformation model of metal mines transferred underground mining from open-pit based on support vector machines[J]. Chinese Journal of Engineering, 2009, 31(8): 945-950. DOI: 10.13374/j.issn1001-053x.2009.08.001
Citation:
LI Zhang-hong, WANG Yun-fei, CAI Mei-feng, MIAO Sheng-jun, FAN Li-ping. Slope deformation model of metal mines transferred underground mining from open-pit based on support vector machines[J]. Chinese Journal of Engineering, 2009, 31(8): 945-950. DOI: 10.13374/j.issn1001-053x.2009.08.001
LI Zhang-hong, WANG Yun-fei, CAI Mei-feng, MIAO Sheng-jun, FAN Li-ping. Slope deformation model of metal mines transferred underground mining from open-pit based on support vector machines[J]. Chinese Journal of Engineering, 2009, 31(8): 945-950. DOI: 10.13374/j.issn1001-053x.2009.08.001
Citation:
LI Zhang-hong, WANG Yun-fei, CAI Mei-feng, MIAO Sheng-jun, FAN Li-ping. Slope deformation model of metal mines transferred underground mining from open-pit based on support vector machines[J]. Chinese Journal of Engineering, 2009, 31(8): 945-950. DOI: 10.13374/j.issn1001-053x.2009.08.001
1. Key Laboratory of the Ministry of Education of China for Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing 100083, China;
2. School of Civil and Environment Engineering, University of Science and Technology Beijing, Beijing 100083, China
A slope deformation model of metal mines transferred underground mining from open-pit based on support vector machines was presented. The model can effectively express the non-linear variation of metal mine open-pit slope deformation caused by underground mining disturbance. In the model the RBF kernel function was utilized to train on-site monitoring data, the cross-validation was employed to choose model parameters, support vectors were achieved with training samples, and then the future deformation was predicted. The model was applied to Xingshan Iron Ore transferred underground mining from open-pit. The results show that the regression value of learning samples is extremely precise and the predicted deformation has a higher precision based on support vector machines. The application of the model, which predicts the deformation with the achieved support vectors, is convenient and it bears a stronger generalization ability.