Rockburst prediction model based on comprehensive weight and extension methods and its engineering application

In view of the dynamic instability of rock mass in high geostress areas during underground engineering excavation, the comprehensive weight and extension methods are adopted to research the rockburst prediction. Firstly, five main influencing factors including uniaxial compressive strength, stress coefficient, brittleness coefficient, elastic energy index, and integrity of rock mass are used as the evaluation indexes of rockburst prediction according to the conditions required for rockburst occurrence. The assessment index system of rockburst intensity is constituted. Secondly, the analytic hierarchy process (AHP) and variation coefficient methods are used to determine the comprehensive weight of evaluation index, and the rockburst prediction model is established based on the extension evaluation method. Thirdly, the parameter programming and numerical calculation of the proposed prediction model are carried out in the MATLAB software. The user visualization execution window and software system of rockburst prediction model are realized. Finally, the software system is applied to the rockburst prediction in the water diversion tunnels of Jiangbian hydropower station and Jinping secondary hydropower station. The prediction results are compared with the actual situation and other evaluation methods. The results show that (i) the establishment of the user visualization window realizes the visualization and systematization of rockburst prediction model, which improves the data import rate and calculation efficiency. (ii) The prediction results of the proposed software system agree well with the actual situation, and they are more accurate than other evaluation methods. (iii) The proposed software system of rockburst prediction can also be used in coal mine, metro, and other underground projects, which has good engineering application values.

[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.