Rockburst prediction and prevention in underground space excavation

Jian Zhou , Yulin Zhang , Chuanqi Li , Haini He , Xibing Li

Underground Space ›› 2024, Vol. 14 ›› Issue (1) : 70 -98.

PDF (11525KB)
Underground Space ›› 2024, Vol. 14 ›› Issue (1) : 70 -98. DOI: 10.1016/j.undsp.2023.05.009

Rockburst prediction and prevention in underground space excavation

Author information +
History +
PDF (11525KB)

Abstract

The technical challenges associated with deep underground space activities have become increasingly significant. Among these challenges, one major concern is the assessment of rockburst risks and the instability of rock masses. Extensive research has been conducted by numerous scholars to mitigate the risks and prevent occurrences of rockburst through various assessment methods. Rockburst incidents commonly occur during the excavation of hard rock in underground environments, posing severe threats to personnel safety, equipment integrity, and operational continuity. Thus, it is crucial to systematically document real cases of rockburst, allowing for a comprehensive understanding of the underlying mechanisms and triggering conditions. This understanding will contribute to the advancement of rockburst prediction and prevention methods. Proper selection of an appropriate rockburst assessment method is a fundamental aspect in underground operations. However, there is a limited number of studies that summarize and compare different prediction and prevention methods of rockburst. This paper aims to address this gap by analyzing global trends using CiteSpace software since 1990. It discusses rockburst classification and characteristics, comprehensively reviews research findings related to rockburst prediction, including empirical, simulation, mathematical modeling, and microseismic monitoring methods. Additionally, the paper presents a compilation of current rockburst prevention measures. Notably, the paper emphasizes the significance of control strategies, which provide key insights into the effective utilization of stored energy within rock. Finally, the paper concludes by suggesting six directions for implementing intelligent management techniques to mitigate hazards during underground operations and reduce the probability of rockburst incidents.

Keywords

Rockburst / Underground space / Scientometric analysis / Characteristic analysis / Rockburst prediction / Rockburst prevention

Cite this article

Download citation ▾
Jian Zhou,Yulin Zhang,Chuanqi Li,Haini He,Xibing Li. Rockburst prediction and prevention in underground space excavation. Underground Space, 2024, 14(1): 70-98 DOI:10.1016/j.undsp.2023.05.009

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Adoko A. C., Gokceoglu C., Wu L., & Zuo Q. J. (2013). Knowledge- based and data-driven fuzzy modeling for rockburst prediction. International Journal of Rock Mechanics and Mining Sciences, 61, 86-95.

[2]

Afraei S., Shahriar K., & Madani S. H. (2019). Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, section 2: Designing classifiers. Tunnelling and Underground Space Technology, 84, 522-537.

[3]

Askaripour M., Saeidi A., Rouleau A., & Mercier-Langevin P. (2022). Rockburst in underground excavations: A review of mechanism, classification, and prediction methods. Underground Space, 7(4), 577-607.

[4]

Bardet J. P. (1989). Finite element analysis of rockburst as surface instability. Computers and Geotechnics, 8(3), 177-193.

[5]

Barton N., Lien R., & Lunde J. (1974). Engineering classification of rock masses for the design of tunnel support. Rock Mechanics, 6(4), 189-236.

[6]

Bergen K. J., Johnson P. A., de Hoop M. V., & Beroza G. C. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science, 363(6433), eaau0323.

[7]

Blake W., & Hedley D. G. F. (2003). Rockbursts:Case studies from North American hard-rock mines. Littleton, CO: Society for Mining, Metallurgy, and Exploration. Inc.

[8]

Brown E. T., & Hoek E. (1980). Underground excavations in rock. CRC Press.

[9]

Cai M., Kaiser P. K., Morioka H., Minami M., Maejima T., Tasaka Y., & Kurose H. (2007). FLAC/PFC coupled numerical simulation of AE in large-scale underground excavations. International Journal of Rock Mechanics and Mining Sciences, 44(4), 550-564.

[10]

Cai M. (2013). Principles of rock support in burst-prone ground. Tunnelling & Underground Space Technology Incorporating Trenchless Technology Research, 36, 46-56.

[11]

Chen B. R., Feng X. T., Li Q. P., Luo R. Z., & Li S. (2015). Rock burst intensity classification based on the radiated energy with damage intensity at Jinping II hydropower station, China. Rock Mechanics and Rock Engineering, 48, 289-303.

[12]

Chen C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for information Science and Technology, 57(3), 359-377.

[13]

Chen C., & Zhou J. (2023). A new empirical chart for coal burst liability classification using Kriging method. Journal of Central South University, 30(4), 1205-1216.

[14]

Chen H. J., Li X. B., & Zhang Y. (2008). Study on application of set pair analysis method to prediction of rockburst. Journal of University of South China, 22(4), 10-14 (in Chinese).

[15]

Cook N. G. W. (1983). Origin of rockbursts. In Rockbursts: prediction and control. Symposium (pp. 1-9).

[16]

Cook N. G. W. (1965). A note on rockbursts considered as a problem of stability. Journal of the Southern African Institute of Mining and Metallurgy, 65(8), 437-446.

[17]

Cook N. G. W. (1963). The basic mechanics of rockbursts. Journal of the Southern African Institute of Mining and Metallurgy, 64(3), 71-81.

[18]

Deng J., & Gu D. S. (2018). Buckling mechanism of pillar rockbursts in underground hard rock mining. Geomechanics and Geoengineering, 13 (3), 168-183.

[19]

Diederichs M. S. (2018). Early assessment of dynamic rupture hazard for rockburst risk management in deep tunnel projects. Journal of the Southern African Institute of Mining and Metallurgy, 118(3), 193-204.

[20]

Dou L., Chen T., Gong S., He H., & Zhang S. (2012). Rockburst hazard determination by using computed tomography technology in deep workface. Safety Science, 50(4), 736-740.

[21]

Dou L., Zhou K., Song S., Cao A., Cui H., Gong S., & Ma X. (2021). Occurrence mechanism, monitoring and prevention technology of rockburst in coal mines. Journal of Engineering Geology, 29(4), 917-932.

[22]

Du K., Li X., Su R., Tao M., Lv S., Luo J., & Zhou J. (2022). Shape ratio effects on the mechanical characteristics of rectangular prism rocks and isolated pillars under uniaxial compression. International Journal of Mining Science and Technology, 32(2), 347-362.

[23]

Du K., Tao M., Li X. B., & Zhou J. (2016). Experimental study of slabbing and rockburst induced by true-triaxial unloading and local dynamic disturbance. Rock Mechanics and Rock Engineering, 49(9), 3437-3453.

[24]

Feng X. T., Chen S., & Zhou H. (2004). Real-time computerized tomography (CT) experiments on sandstone damage evolution during triaxial compression with chemical corrosion. International Journal of Rock Mechanics and Mining Sciences, 41(2), 181-192.

[25]

Feng X. T., Xue Y. S., & Feng G. L. (2012). Mechanism, warning and dynamic control of rockburst evolution process pp. ISRM-ARMS7. ISRM International Symposium-Asian Rock Mechanics Symposium. ISRM.

[26]

Feng X. T., Chen B. R., Zhang C. Q., Li S. J., & Wu S. Y. (2013). Mechanism, Warning and Dynamical Control of Rockburst Evolution Process. Beijing:Science Press (pp.380-391). Beijing: Science Press (in Chinese).

[27]

Gu R. (2013). Distinct element model analyses of unstable failures in underground coal mines. Colorado School of Mines.

[28]

Guo C., Zhang Y. S., Deng H., Su Z., & Sun D. (2011). Study on rock burst prediction in the deep-buried tunnel at Gaoligong Mountain based on the rock proneness. Geotech Invest Survey, 39(10), 8-13, in Chinese).

[29]

He S., Song D., Mitri H., He X., Chen J., Li Z., Xue Y., & Chen T. (2021). Integrated rockburst early warning model based on fuzzy comprehensive evaluation method. International Journal of Rock Mechanics and Mining Sciences, 142, 104767.

[30]

Heal D. (2010). Observations and analysis of incidences of rockburst damage in underground mines [Ph. D. Thesis]. Australia: University of Western Australia.

[31]

Heal D., Hudyma M., & Potvin Y. (2006). Evaluating rockburst damage potential in underground mining. Golden Rocks 2006, The 41st US Symposium on Rock Mechanics (USRMS). OnePetro.

[32]

Hou F. L., & Wang M. Q. (1989). Criterion and prevention measures on rockburst in circular tunnel. Proceedings of the 2nd national rock mechanics and engineering (Chinese Journal of Rock Mechanics and Engineering). Beijing: Knowledge Press (in Chinese).

[33]

Huang B. X., Zhang N., & Jing H. W. (2020). Large deformation theory of surrounding rock rheology and structural instability of deep mining roadway. Chinese Journal of Coal, 45(03), 911-926 (in Chinese).

[34]

Hudson J. A., & Feng X. T. (2015). Rock engineering risk (Vol. 1). CRC Press.

[35]

Ishida Shimizu Murata & Kanagawa (2009). Importance of inhomogeneity in rock fracturing deduced from distinct element simulation and insitu direct shear test. In Proceedings of the 7th International Symposium on Rockburst and Seismicity in Mines (Keynote Presentation), Dalian, China (pp. 3-18).

[36]

Jia Y. P., Lv Q., Shang Y. Q., Du L. L., & Zhi M. M. (2014). Rockburst prediction based on rough set and ideal point method. Journal of Zhejiang University (Engineering Science), 48(3), 498-503 (in Chinese).

[37]

Jiang L., Kong P., Zhang P., Shu J., Wang Q., Chen L., & Wu Q. (2020). Dynamic analysis of the rock burst potential of a longwall panel intersecting with a fault. Rock Mechanics and Rock Engineering, 53, 1737-1754.

[38]

Jiang Q., Feng X. T., Xiang T. B., & Su G. S. (2010). Rockburst characteristics and numerical simulation based on a new energy index: A case study of a tunnel at 2,500 m depth. Bulletin of engineering geology and the environment, 69, 381-388.

[39]

Jiao J. K., & Ju W. J. (2021). Impact failure mechanism of roadway anchorage bearing structure under dynamic load disturbance. Journal of China Coal Society, 46, 94-105 (in Chinese).

[40]

Jing H. W., Yin Q., Zhu D., Sun Y. J., & Wang B. (2020). Experimental study on the whole process of instability and failure of anchorage structure in surrounding rock of deep-buried roadway. Journal of China Coal Society, 45(3), 889-901 (in Chinese).

[41]

Jin Y. N., Li X. B., Liu P. J., & Guo Y. (2017). Predictive liability of the rockburst classification based on the improved unascertained clustering model. Journal of Safety and Environment, 17(1), 12-16.

[42]

Kaiser P. K., & Cai M. (2012). Design of rock support system under rockburst condition. Journal of Rock Mechanics and Geotechnical Engineering, 4(3), 215-227.

[43]

Kaiser P. K., McCreath D., & Tannant D. (1996). Canadian Rockburst Support Handbook. Geomechanics Research Centre: Laurentian University.

[44]

Kaiser P. K., Tannant D. D., McCreath D. R., & Jesenak P. (1992). Rockburst damage assessment procedure. In International symposium on rock support (pp. 639-647).

[45]

Keneti A., & Sainsbury B. A. (2018). Review of published rockburst events and their contributing factors. Engineering Geology, 246, 361-373.

[46]

Ke W. (2021). Overview of State-of-Art of Rockburst Prediction and Prevention Techniques for Deep-buried Tunnels. Tunnel Construction, 41(02), 212-224.

[47]

Kidybin´ski A. (1981). Bursting liability indices of coal. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 18, No. 4, pp. 295-304). Pergamon.

[48]

Kolymbas D. (2005). The new Austrian tunnelling method. Tunnelling and Tunnel Mechanics: A Rational Approach to Tunnelling, 171-175.

[49]

Leger J. P. (1991). Trends and causes of fatalities in South African mines. Safety science, 14(3-4), 169-185.

[50]

Li C., Zhou J., Armaghani D. J., & Li X. (2021). Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques. Underground Space, 6(4), 379-395.

[51]

Li D., Liu Z., Armaghani D. J., Xiao P., & Zhou J. (2022a). Novel ensemble tree solution for rockburst prediction using deep forest. Mathematics, 10(5), 787.

[52]

Li D., Liu Z., Xiao P., Zhou J., & Armaghani D. J. (2022b). Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization. Underground Space, 7(5), 833-846.

[53]

Li D., Liu Z., Armaghani D. J., Xiao P., & Zhou J. (2022c). Novel ensemble intelligence methodologies for rockburst assessment in complex and variable environments. Scientific Reports, 12(1), 1-23.

[54]

Li G. F., Cai J., & Guo Z. B. (2007). Research and application of bolt and grouting combined support technology to deep mine soft rock roadway. Coal Science and Technology, 35(4), 44-46 (in Chinese).

[55]

Li L. G., Wensheng X., Yingnian X., & Yuanhan W. (2001). Experimental study on simulation materials of rockburst. Journal- Huazhong University of Science and Technology Chinese Edition, 29(6), 80-82.

[56]

Li N., Feng X., & Jimenez R. (2017a). Predicting rock burst hazard with incomplete data using Bayesian networks. Tunnelling and Underground Space Technology, 61, 61-70.

[57]

Li N., & Jimenez R. (2018). A logistic regression classifier for long-term probabilistic prediction of rock burst hazard. Natural Hazards, 90(1), 197-215.

[58]

Li X., Yi W., Chi H. L., Wang X., & Chan A. P. (2018). A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Automation in Construction, 86, 150-162.

[59]

Li X. B., & Dong L. J. (2014). An efficient closed-form solution for acoustic emission source location in three-dimensional structures. AIP Advances, 4(2), 027110.

[60]

Li X. B., Gong F. Q., Wang S. F., Li D. Y., Tao M., Zhou J., Huang L. Q., Ma C. D., Du K., & Feng F. (2019). Coupled static-dynamic loading mechanical mechanism and dynamic criterion of rockburst in deep hard rock mines. Chinese Journal of Rock Mechanics and Engineering, 38(4), 708-723 (in Chinese).

[61]

Li X. B., & Gu D. S. (2002). Disaster control and fracture mutagenesis of high stress in mining of deep hard deposits. Paper presented at the 175th Xiangshan Scientific Conference: Frontiers and the Future of Science: Volume 6. Beijing: China Environmental Science Press.

[62]

Li X. B., Zhou J., Wang S. F., & Liu B. (2017b). Review and practice of deep mining for solid mineral resources. The Chinese Journal of Nonferrous Metals, 27(6), 1236 (in Chinese).

[63]

Liang W., Zhao G., Wu H., & Dai B. (2019). Risk assessment of rockburst via an extended MABAC method under fuzzy environment. Tunnelling and Underground Space Technology, 83, 533-544.

[64]

Liu J., Liu C., Yao Q., & Si G. (2020). The position of hydraulic fracturing to initiate vertical fractures in hard hanging roof for stress relief. International Journal of Rock Mechanics and Mining Sciences, 132, 104328.

[65]

Liu R., Ye Y., Hu N., Chen H., & Wang X. (2019). Classified prediction model of rockburst using rough sets-normal cloud. Neural Computing and Applications, 31, 8185-8193.

[66]

Lu A. H., Mao X. B., & Liu H. S. (2008). Physical simulation of rock burst induced by stress waves. Journal of China University of Mining and Technology, 18(3), 401-405.

[67]

Ma T. H., Tang C. A., Liu F., Zhang S. C., & Feng Z. Q. (2021). Microseismic monitoring, analysis and early warning of rockburst. Geomatics, Natural Hazards and Risk, 12(1), 2956-2983.

[68]

Ma T. H., Tang C. A., Tang L. X., Zhang W. D., & Wang L. (2015). Rockburst characteristics and microseismic monitoring of deep-buried tunnels for Jinping II Hydropower Station. Tunnelling and Underground Space Technology, 49, 345-368.

[69]

Mark C. (2016). Coal bursts in the deep longwall mines of the United States. International Journal of Coal Science & Technology, 3(1), 1-9.

[70]

Merghadi A., Yunus A. P., Dou J., Whiteley J., ThaiPham B., Bui D. T.,... Abderrahmane B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225.

[71]

Meng B., Jing H. W., Chen K. F., Su H. J., Yang S. Q., & Li Y. H. (2012). Shear and slip failure mechanism and control of tunnels with weak surrounding rock. Chinese Journal of Geotechnical Engineering, 34(12), 2255-2262.

[72]

Meng B., Jing H., Chen K., & Su H. (2013). Failure mechanism and stability control of a large section of very soft roadway surrounding rock shear slip. International Journal of Mining Science and Technology, 23(1), 127-134.

[73]

Meng B., Jing H., Yang S., Wu Y., Wang Y., & Huang Y. (2021). Experimental investigation on shear behavior of intact sandstones under constant normal stiffness conditions. International Journal of Geomechanics, 21(2), 04020259.

[74]

Meng Q. B., Kong L. H., Wei L. C., Shen H. L., & Yang Y. M. (2011). Research Status and Prospect on The Support of Soft Rock Roadway Engineering in Coal Mine. Coal, 01, 1-6 (in Chinese).

[75]

Min Q., Lu Y., Liu Z., Su C., & Wang B. (2019). Machine learning based digital twin framework for production optimization in petro- chemical industry. International Journal of Information Management, 49, 502-519.

[76]

Mitri H. S. (2007). Assessment of horizontal pillar burst in deep hard rock mines. International Journal of Risk Assessment and Management, 7(5), 695-707.

[77]

Mu¨ ller W. (1991). Numerical simulation of rock bursts. Mining Science and Technology, 12(1), 27-42.

[78]

Oliver L. C., Sampara P., Pearson D., Martell J., & Zarnke A. M. (2022). Sarcoidosis in Northern Ontario hard-rock miners: A case series. American Journal of Industrial Medicine, 65(4), 268-280.

[79]

Pan J. F., Liu S. H., Xia Y. X., & Gao J. M. (2021). Impact failure characteristics and support method of roadway in broken coal seam with large dip angle. Chinese Journal of Mining and Safety Engineering, 38(05), 946-953.

[80]

Pan Y., & Zhang L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, 103517.

[81]

Pei Q. T., Li H. B., Liu Y. Q., & Niu J. T. (2013). Rockburst prediction based on a modified grey evaluation model. Chinese Journal of Rock Mechanics and Engineering, 32(10), 2088-2093.

[82]

Perol T., Gharbi M., & Denolle M. (2018). Convolutional neural network for earthquake detection and location. Science Advances, 4(2), e1700578.

[83]

Pu Y., Apel D. B., Liu V., & Mitri H. (2019). Machine learning methods for rockburst prediction-state-of-the-art review. International Journal of Mining Science and Technology, 29(4), 565-570.

[84]

Qiao C., Guo Y. H., & Li C. H. (2021). Study on rock burst prediction of deep buried tunnel based on cusp catastrophe theory. Geotechnical and Geological Engineering, 39(1), 1-15.

[85]

Qiu S. L., Feng X. T., Zhang C. Q., & Wu W. P. (2011). Development and validation of rockburst vulnerability index (RVI) in deep hard rock tunnels. Chinese Journal of Rock Mechanics and Engineering., 30 (6), 1126-1141 (in Chinese).

[86]

Qiu Y. G., & Zhou J. (2023). Short-term rockburst prediction in underground project: Insights from an explainable and interpretable ensemble learning model. Acta Geotechnica, 1-31. https://doi.org/10.1007/s11440-023-01988-0.

[87]

Rehman H., Naji A. M., Ali W., Junaid M., Abdullah R. A., & Yoo H. K. (2020). Numerical evaluation of new Austrian tunneling method excavation sequences: A case study. International Journal of Mining Science and Technology, 30(3), 381-386.

[88]

Rehman H., Naji A. M., Nam K., Ahmad S., Muhammad K., & Yoo H. K. (2021). Impact of construction method and ground composition on headrace tunnel stability in the Neelum-Jhelum Hydroelectric Project: A case study review from Pakistan. Applied Sciences, 11(4), 1655.

[89]

Russenes B. F. (1974). Analysis of rock spalling for tunnels in steep valley sides [Master’s thesis, Norwegian Institute of Technology].

[90]

Shi X. Z., Zhou J., Dong L., Hu H. Y., Wang H. Y., & Chen S. R. (2010). Application of unascertained measurement model to prediction of classification of rockburst intensity. Chinese Journal of Rock Mechanics and Engineerin, 29(S1), 2720-2726 (in Chinese).

[91]

Shirani F. R., & Taheri A. (2019). Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques. Engineering with Computers, 35(2), 659-675.

[92]

Simser B. P. (2019). Rockburst management in Canadian hard rock mines. Journal of Rock Mechanics and Geotechnical Engineering, 11(5), 1036-1043.

[93]

Singh S. P. (1987). The influence of rock properties on the occurrence and control of rockbursts. Mining Science and Technology, 5(1), 11-18.

[94]

Sun Y., Li G., Zhang J., & Huang J. (2021). Rockburst intensity evaluation by a novel systematic and evolved approach: Machine learning booster and application. Bulletin of engineering geology and the environment, 80(11), 8385-8395.

[95]

Suorineni F. T., Hebblewhite B., & Saydam S. (2014). Geomechanics challenges of contemporary deep mining: A suggested model for increasing future mining safety and productivity. Journal of the Southern African Institute of Mining and Metallurgy, 114(12), 1023-1032.

[96]

Tajdus´ A. (1997). Estimation of rockburst hazard basing on 3D stress field analysis. Ratio, 1(2), 3.

[97]

Tang C. (1997). Numerical simulation of progressive rock failure and associated seismicity. International Journal of Rock Mechanics and Mining Sciences, 34(2), 249-261.

[98]

Tang S., Shelden D. R., Eastman C. M., Pishdad-Bozorgi P., & Gao X. (2019). A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Automation in Construction, 101, 127-139.

[99]

Tan Y. A., Guangzhong S., & Zhi G. U. O. (1991). A composite index Krb criterion for the ejection characteristics of the burst rock. Chinese Journal of Geology, 26(2), 193-200.

[100]

Tao Z. Y. (1988). Support design of tunnels subjected to rockbursting pp. ISRM-IS. ISRM International Symposium. ISRM.

[101]

Turchaninov I. A., Markov G. A., Gzovsky M. V., Kazikayev D. M., Frenze U. K., Batugin S. A., & Chabdarova U. I. (1972). State of stress in the upper part of the Earth’s crust based on direct measurements in mines and on tectonophysical and seismological studies. Physics of the Earth and Planetary Interiors, 6(4), 229-234.

[102]

Wang B., Wang W., Zhao F., Fan & Tang (2014). Research on bolt anchoring effect based on self-supporting characteristics of roadway surrounding rock. Geotechnical Mechanics, 35(7), 8.

[103]

Wang H., Shi R., Lu C., Jiang Y., Deng D., & Zhang D. (2019a). Investigation of sudden faults instability induced by coal mining. Safety Science, 115, 256-264.

[104]

Wang L. S., Li T. B., Xu J., Xu L. S., Jin X. G., & Li Y. L. (1999). Rockburst and its intensity classification of Erlangshan highway tunnel. Highway, 28(2), 41-45.

[105]

Wang M., Liu Q., Wang X., Shen F., & Jin J. (2020). Prediction of rockburst based on multidimensional connection cloud model and set pair analysis. International Journal of Geomechanics, 20(1), 04019147.

[106]

Wang S. M., Zhou J., Li C. Q., Armaghani D. J., Li X. B., & Mitri H. S. (2021). Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques. Journal of Central South University, 28(2), 527-542.

[107]

Wang S. Y., Lam K. C., Au S. K., Tang C. A., Zhu W. C., & Yang T. H. (2006). Analytical and numerical study on the pillar rockbursts mechanism. Rock Mechanics and Rock Engineering, 39, 445-467.

[108]

Wang X., Li S., Xu Z., Xue Y., Hu J., Li Z., & Zhang B. (2019b). An interval fuzzy comprehensive assessment method for rock burst in underground caverns and its engineering application. Bulletin of Engineering Geology and the Environment, 78, 5161-5176.

[109]

Wu M., Ye Y., Wang Q., & Hu N. (2022). Development of rockburst research: A comprehensive review. Applied Sciences, 12(3), 974.

[110]

Wu Y. Z., Fu Y. K., He J., Chen J. Y., Chu X. W., & Meng X. Z. (2021). Principle and technology of ‘‘pressure relief-support- protection” collaborative prevention and control in deep rock burst roadway. Journal of China Mine Society, 46(1), 132-144.

[111]

Xi Y. L., Xu Y. Z., & Wang L. C. (2017). Study and countermeasures on key technologies for long deeply buried tunnel of Qireha Starr Hydropower Station. Water Resources and Hydropower Engineering, 48(10), 26-30.

[112]

Xu Y. N., Xu W. S., Wang Y. H., Tham L. G., & Lee P. K. K. (2002). Simulation testing and mechanism studies on rockburst. Chinese Journal of Rock Mechanics and Engineering, 21, 1462-1466.

[113]

Yang Z. Y., Liu C., Zhu H. Z., Xie F. X., Dou L. M., & Chen J. H. (2019). Mechanism of rock burst caused by fracture of key strata during irregular working face mining and its prevention methods. Journal of Mining Science and Technology: English Edition, 29(6), 9.

[114]

Yang S. Q. (1993). An experimental study on rockburst mechanism around tunnels by physical simulation. Journal of Wuhan University of Hydraulic and Electric Engineering, 26(2), 160-166.

[115]

Yin X., Liu Q., Pan Y., Huang X., Wu J., & Wang X. (2021). Strength of stacking technique of ensemble learning in rockburst prediction with imbalanced data: Comparison of eight single and ensemble models. Natural Resources Research, 30, 1795-1815.

[116]

Yu S., & Ma J. (2021). Deep learning for geophysics: Current and future trends. Reviews of Geophysics, 59(3), e2021RG000742.

[117]

Zhang C. Q., Lu J. J., Chen J., Zhou H., & Yang (2017). Discussion on rock burst proneness indexes and their relation. Rock and Soil Mechanics, 38(5), 1397-1404.

[118]

Zhang C. Q., Zhou H., & Feng X. T. (2011). An index for estimating the stability of brittle surrounding rock mass: FAI and its engineering application. Rock Mechanics and Rock Engineering, 44(4), 401-414.

[119]

Zhang G., Pan Y., Zhang L., & Tiong R. L. K. (2020a). Cross-scale generative adversarial network for crowd density estimation from images. Engineering Applications of Artificial Intelligence, 94, 103777.

[120]

Zhang J., Wang Y., Sun Y., & Li G. (2020b). Strength of ensemble learning in multiclass classification of rockburst intensity. International Journal for Numerical and Analytical Methods in Geomechanics, 44(13), 1833-1853.

[121]

Zhang L., Zhang X., Wu J., Zhao D., & Fu H. (2020c). Rockburst prediction model based on comprehensive weight and extension methods and its engineering application. Bulletin of Engineering Geology and the Environment, 79, 4891-4903.

[122]

Zhao H., Chen B., & Zhu C. (2021). Decision tree model for rockburst prediction based on microseismic monitoring. Advances in Civil Engineering, 2021, 1-14.

[123]

Zhou J. (2015a). Supervised learning methods for strain-based rockburst prediction and crater depth estimation. Ph.D thesis, Changsha, Central South University.

[124]

Zhou J., Chen C., Wang M., & Khandelwal M. (2021a). Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors. International Journal of Mining Science and Technology, 31(5), 99-812.

[125]

Zhou J., Chen C., Wei C., & Du K. (2022a). An Improved Connection Cloud Model of an Updated Database: A Multicriteria Uncertainty Model for Coal Burst Liability Evaluation. Natural Resources Research, 31(3), 1687-1704.

[126]

Zhou J., Koopialipoor M., Li E., & Armaghani D. J. (2020). Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system. Bulletin of Engineering Geology and the Environment, 79(8), 4265-4279.

[127]

Zhou J., Li X. B., & Mitri H. S. (2015b). Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Natural Hazards, 79(1), 291-316.

[128]

Zhou J., Li X., & Mitri H. S. (2016a). Classification of rockburst in underground projects: Comparison of ten supervised learning meth- ods. Journal of Computing in Civil Engineering, 30(5), 04016003.

[129]

Zhou J., Li X. B., & Mitri H. S. (2018). Evaluation method of rockburst: State-of-the-art literature review. Tunnelling and Underground Space Technology, 81, 632-659.

[130]

Zhou J., Li X. B., & Shi X. Z. (2012). Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Safety Science, 50(4), 629-644.

[131]

Zhou J., Guo H., Koopialipoor M., Jahed Armaghani D., & Tahir M. M. (2021b). Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm. Engineering with Computers, 37(3), 1679-1694.

[132]

Zhou J., Shen X., Qiu Y., Li E., Rao D., & Shi X. (2021c). Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 7(3), 1-18.

[133]

Zhou J., Shen X., Qiu Y., Shi X., & Khandelwal M. (2022b). Cross- correlation stacking-based microseismic source location using three metaheuristic optimization algorithms. Tunnelling and Underground Space Technology, 126, 104570.

[134]

Zhou J., Shi X. Z., Dong L., Hu H. Y., & Wang H. Y. (2010). Fisher discriminant analysis model and its application for prediction of classification of rockburst in deep-buried long tunnel. Journal of Coal Science and Engineering (China), 16(2), 144-149.

[135]

Zhou J., Shi X. Z., Huang R. D., Qiu X. Y., & Chen C. (2016b). Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines. Transactions of Nonferrous Metals Society of China, 26(7), 1938-1945.

[136]

Zhu Q., Zhao X., & Westman E. (2021). Review of the evolution of mining-induced stress and the failure characteristics of surrounding rock based on microseismic tomography. Shock and Vibration, 2021, 1-19.

[137]

Zhu S., Feng Y., Jiang F., & Liu J. (2018). Mechanism and risk assessment of overall-instability-induced rockbursts in deep island longwall panels. International Journal of Rock Mechanics and Mining Sciences, 106, 342-349.

[138]

Zhu W. C., Li Z. H., Zhu L., & Tang C. A. (2010). Numerical simulation on rockburst of underground opening triggered by dynamic disturbance. Tunnelling and Underground Space Technology, 25(5), 587-599.

[139]

Zubelewicz A., & Mroz Z. (1983). Numerical simulation of rock burst processes treated as problems of dynamic instability. Rock Mechanics and Rock Engineering, 16(4), 253-274.

[140]

Wen C.P., 2008. Application of attribute synthetic evaluation system in prediction of possibility and classification of rockburst. Engineering Mechanics. 25 (6), 153-158. (in Chinese)

AI Summary AI Mindmap
PDF (11525KB)

689

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/