Rockburst prediction model based on comprehensive weight and extension methods and its engineering application
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Xiangyu Zhang | Hao Fu | Jing Wu | Lewen Zhang | Dukun Zhao | Le-wen Zhang | Jing Wu | Dukun Zhao | Hao Fu | Xiang-yu Zhang
[1] Hani S. Mitri,et al. Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods , 2016, J. Comput. Civ. Eng..
[2] J.-A. Wang,et al. Comprehensive prediction of rockburst based on analysis of strain energy in rocks , 2001 .
[3] Linming Dou,et al. Rockburst mechanism and control in coal seam with both syncline and hard strata , 2019, Safety Science.
[4] Hui Zhou,et al. Experimental study of factors affecting fault slip rockbursts in deeply buried hard rock tunnels , 2017, Bulletin of Engineering Geology and the Environment.
[5] Jiang He,et al. Rock burst assessment and prediction by dynamic and static stress analysis based on micro-seismic monitoring , 2017 .
[6] Chen Hai. A model for prediction of rockburst by artificial neural network , 2002 .
[7] Meifeng Cai,et al. Rock burst prediction based on in-situ stress and energy accumulation theory , 2016 .
[8] Mostafa Sharifzadeh,et al. Reinforcement selection for deep and high-stress tunnels at preliminary design stages using ground demand and support capacity approach , 2018, International Journal of Mining Science and Technology.
[9] Lin Wang,et al. Prediction of rock burst in underground caverns based on rough set and extensible comprehensive evaluation , 2019, Bulletin of Engineering Geology and the Environment.
[10] Peter K. Kaiser,et al. Numerical simulation of cumulative damage and seismic energy release during brittle rock failure-Part I: Fundamentals , 1998 .
[11] Hao Wu,et al. Risk assessment of rockburst via an extended MABAC method under fuzzy environment , 2019, Tunnelling and Underground Space Technology.
[12] Manchao He,et al. Rockburst mechanism research and its control , 2018, International Journal of Mining Science and Technology.
[13] Wei Gao,et al. Forecasting of rockbursts in deep underground engineering based on abstraction ant colony clustering algorithm , 2015, Natural Hazards.
[14] Fuxing Jiang,et al. Rockburst mechanism in soft coal seam within deep coal mines , 2017 .
[15] Roohollah Shirani Faradonbeh,et al. Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques , 2018, Engineering with Computers.
[16] Ji‐Quan Shi,et al. A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring , 2018, Tunnelling and Underground Space Technology.
[17] Xia-Ting Feng,et al. Rockburst characteristics and numerical simulation based on a new energy index: a case study of a tunnel at 2,500 m depth , 2010 .
[18] Li Xibing,et al. Prediction of rockburst classification using Random Forest , 2013 .
[19] Gexiang Zhang,et al. A Hybrid Classifier Based on Rough Set Theory and Support Vector Machines , 2005, FSKD.
[20] Meifeng Cai,et al. Prediction and prevention of rockburst in metal mines – A case study of Sanshandao gold mine , 2016 .
[21] Xiuzhi Shi,et al. Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines , 2012 .
[22] Xiating Feng,et al. Characteristic microseismicity during the development process of intermittent rockburst in a deep railway tunnel , 2019 .
[23] Qiu Dao. Study on Rockburst Prediction and Prevention in Deep and Over-Length Highway Tunnel , 2006 .
[24] K. Shahriar,et al. Statistical assessment of rock burst potential and contributions of considered predictor variables in the task , 2018 .
[25] Wen Cai,et al. Basic theory and methodology on Extenics , 2013 .
[26] Lin-ming Dou,et al. Passive seismic tomography for rockburst risk identification based on adaptive-grid method , 2019, Tunnelling and Underground Space Technology.
[27] Hui Zhou,et al. Analysis of rockburst mechanisms induced by structural planes in deep tunnels , 2015, Bulletin of Engineering Geology and the Environment.
[28] Li Wu,et al. The Comprehensive Prediction Model of Rockburst Tendency in Tunnel Based on Optimized Unascertained Measure Theory , 2019, Geotechnical and Geological Engineering.
[29] Xiating Feng,et al. In situ monitoring of rockburst nucleation and evolution in the deeply buried tunnels of Jinping II hydropower station , 2012 .
[30] Ming Tao,et al. Experimental simulation investigation on rockburst induced by spalling failure in deep circular tunnels , 2018, Tunnelling and Underground Space Technology.
[31] Xia-Ting Feng,et al. Rockmass damage development following two extremely intense rockbursts in deep tunnels at Jinping II hydropower station, southwestern China , 2013, Bulletin of Engineering Geology and the Environment.
[32] V. Mansurov. Prediction of rockbursts by analysis of induced seismicity data , 2001 .
[33] Wen Chang-ping. APPLICATION OF ATTRIBUTE SYNTHETIC EVALUATION SYSTEM IN PREDICTION OF POSSIBILITY AND CLASSIFICATION OF ROCKBURST , 2008 .
[34] M. Caia,et al. A review of mining-induced seismicity in China , 2007 .
[35] Li Wu,et al. Knowledge-based and data-driven fuzzy modeling for rockburst prediction , 2013 .
[36] Zilong Zhou,et al. Failure mechanism and coupled static-dynamic loading theory in deep hard rock mining: A review , 2017 .
[37] Shang Yuequan,et al. ROCKBURST PREDICTION USING PARTICLE SWARM OPTIMIZATION ALGORITHM AND GENERAL REGRESSION NEURAL NETWORK , 2013 .
[38] Sushil Kumar,et al. Analytic hierarchy process: An overview of applications , 2006, Eur. J. Oper. Res..
[39] N. Xu,et al. Discussions on rockburst and dynamic ground support in deep mines , 2019, Journal of Rock Mechanics and Geotechnical Engineering.
[40] Ning Li,et al. Predicting rock burst hazard with incomplete data using Bayesian networks , 2017 .
[41] F. Gong,et al. Experimental Investigation of Strain Rockburst in Circular Caverns Under Deep Three-Dimensional High-Stress Conditions , 2018, Rock Mechanics and Rock Engineering.
[42] Kourosh Shahriar,et al. Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 1: Literature review and data preprocessing procedure , 2019, Tunnelling and Underground Space Technology.