Prediction and Application of Mine Roadway Surrounding Rock Deformation Based on AdaBoost-GA-ELM-Model
Authors
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Civil Engineering College, Chongqing Three Gorges University, Chongqing 404 100 ,CN
Qiang Yue -
Geological Engineering and Surveying College, Chang’an University, Xi’an 710 054 ,CNWu Shuang
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Department of Building and Environmental Safety, Chongqing Vocational Institute of Safety &Technology, Chongqing 404 100 ,CNLiu Chaoqiong
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College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610 059 ,CNLi Shaohong
Keywords:
Mine Roadway Engineer, Surrounding Rock Deformation, ELM, Genetic Algorithm, AdaBoost Algorithm.Abstract
Aiming at the shortcomings of one-sole-model with low accuracy and instability in the deformation prediction for mine roadway surrounding rock, this article comes up with an AdaBoost-GA-ELM model, which combines the ideas of AdaBoost algorithm, genetic algorithm and extreme learning machine, is proposed. The verification of engineering example about trough roof and floor section, I01091004 working surface, Tun-Bao coal mine shows that the AdaBoost-GA-ELM model has almost equal shares in the area of mine roadway surrounding rock deformation, which can bring gratifying prediction results, compared to GA-ELM, GA-BP and gray model, the prediction accuracy of which has a better effect, containing certain value for engineering application.
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References
Zhao, H. B. (2005): “Predicting the surrounding deformations of tunnel using support vector machine.” Chinese Journal of Rock Mechanics and Engineering, 2005, 24(4), 649-652.
Qi, S., Zhou, D. and Wang, L., et. al. (2013): “Deformation prediction of tunnel surrounding rock based on the Grey-Markov chain.” Modern Tunneling Technology, 2013, 50(1), 80-86.
Wei, Yong (2013): “Surrounding rock deformation monitoring technology under complex geological conditions [J].” Coal Mine Modernization, 2013, 131(2):38-40.
Kang, Hongpu, Yan, Lixin and Guo, Xiangping, et. al. (2012): “Characteristics Of Surrounding Rock Deformation And Reinforcement Technology Of Retained Entry In Working Face With Multi-Entry Layout [J].” Chinese Journal of Rock Mechanics And Engineering, 2012, 31(10):2022-2036.
Kang, Hongpu, Niu, Duolong and Zhang, Zhen, et. al. (2010): “Deformation Characteristics Of Surrounding Rock And Supporting Technology Of Gob-Side Entry Retaining In Deep Coal Mine [J].” Chinese Journal of Rock Mechanics And Engineering, 2010, 29(10):1977-1987.
Guo, Zhiwei (2017): “Application Of Grey System Gm(1,1)In Prediction Of Roadway Deformation [J].” Coal and Chemical Industry, 2017, 40(11):75-77.
Guo, Long (2015): “Experimental Modeling Study Of Tunnel Surrounding Rockdeformation Laws In Wangjiazhai Coal Mine [J].” Coal Technology, 2015, 34(12):78-80.
Jing, Hongwen, Wu, Junhao and Ma, Bo, et. al. “Prediction Model And Its Application Of Deep Mine Tunnel Surrounding Rock Deformation Based On Fuzzy-Gray System [J].” Journal of China Coal Society, 2012, 37(7):1099-1104.
Yin, Guangzhi, Li, Minghui and Li, Wenpu, et. al. “Model Of Coal Gas Permeability Prediction Based On Improved Bp Neural Network [J].” Rock and Soil Mechanics, 2013, 38(7):1179-1184.
Kang, Hongpu, Si, Linpo and Su, Bo (2010): “Bore Hole Observation Methods In Coal And Rockmass And Their Applications [J].” Journal of China Coal Society, 2010, 35(12):1949-1956.
Zhang, Z. Q., Li, H. Y. and Kang, C., et. al. (2014): “Prediction of surrounding rock deformation of the Daxiangling tunnel in fault zones using the GA-BP nerve network technique.” Modern Mine Tunneling Technology, 2014, 51(2), 83-89.
Wei, J., Qi, J., Wu, Y., Lu, Y. L. and Wang, L. (2013): “Prediction of the Deformation of the Surrounding Rock around tunnels by GA-Bp Network Model.” Applied Mechanics and Materials, 2013 (256), 1157-1160.
Han-Ying, C., Pu-Zhen, G. and Si-Chao, T., et. al. (2014): “Prediction method of flow instability based on multi-objective optimized extreme learning machine.” Acta Phys. Sin. 2014, 63(20).
Chun-Tao, Z., Qian-Li, M. and Hong, P. (2010): “Chaotic time series prediction based on information entropy optimized parameters of phase space reconstruction.” Acta Phys. Sin. 2010, 59(11):7623-7629.
Jinna-Lu, Hongping, Hu and Yanping, Bai (2015): “Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm.” Neurocomputing. 2015, (152): 305-315.
