The Rock Burst Hazard Evaluation Using Statistical Learning Approaches

State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China College of Water Resources and Hydropower, Sichuan University, Chengdu 610065, China Information Research Institute, Ministry of Emergency Management, Beijing 100029, China Sichuan Coal Industry Group Limited Liability Company, Chengdu 610091, China China Coal Technology and Engineering Group Chongqing Research Institute, Chongqing 400037, China College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China

[1]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[2]  D. Apel,et al.  A Principal Component Analysis/Fuzzy Comprehensive Evaluation for Rockburst Potential in Kimberlite , 2018, Pure and Applied Geophysics.

[3]  Gong Jia ROCKBURST TENDENCY PREDICTION BASED ON AHP-TOPSIS EVALUATION MODEL , 2014 .

[4]  S. P. Singh,et al.  Burst energy release index , 1988 .

[5]  Yves Potvin,et al.  Rockburst And Seismic Activity In Underground Australian Mines - An Introduction To A New Research Project , 2000 .

[6]  Qian Qi,et al.  Definition,mechanism,classification and quantitative forecast model for rockburst and pressure bump , 2014 .

[7]  Yuanyuan Pu,et al.  Evaluation of burst liability in kimberlite using support vector machine , 2018, Acta Geophysica.

[8]  Li Xiang Fisher discriminant analysis model of rock burst prediction and its application in deep hard rock engineering , 2009 .

[9]  Jia Yao-dong,et al.  STUDY ON ROCK MECHANICS IN DEEP MINING ENGINEERING , 2005 .

[10]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[11]  Zhao Hong-bo Classification of engineering rock based on support vector machine , 2002 .

[12]  J. Koenderink Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[13]  Yuanyuan Pu,et al.  Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier , 2019, Tunnelling and Underground Space Technology.

[14]  Yuanyuan Pu,et al.  Applying Machine Learning Approaches to Evaluating Rockburst Liability: A Comparation of Generative and Discriminative Models , 2019, Pure and Applied Geophysics.

[15]  Xiuzhi Shi,et al.  Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines , 2012 .

[16]  A. Kidybiński,et al.  Bursting liability indices of coal , 1981 .

[17]  Hiroshi Morioka,et al.  FLAC/PFC coupled numerical simulation of AE in large-scale underground excavations , 2007 .