|
|
|
| A review of long-term and short-term rockburst risk evaluations in deep hard rock |
| LIANG Weizhang，ZHAO Guoyan |
| (School of Resources and Safety Engineering，Central South University，Changsha，Hunan 410083，China) |
|
|
|
|
Abstract Rockburst has become one of the most serious threats in deep hard rock engineering. Accurately evaluating the risk of rockburst can avoid unnecessary casualties and property losses. In order to understand the research status of long-term and short-term rockburst risk evaluation methods in deep hard rock，the relevant literature at home and abroad is reviewed. First，the long-term and short-term rockburst risk evaluation methods are summarized and classified. Then，the advantages and disadvantages of existing evaluation methods are analyzed. On this basis，the future development directions of long-term and short-term rockburst risk evaluations are further proposed to improve the evaluation level of rockburst risk in hard rock. The results show that the long-term rockburst risk evaluation methods can be summarized as single-index empirical criterion method，multi-index aggregation method，uncertainty analysis method，comprehensive ranking method，machine learning method，numerical simulation method and catastrophe theory，and that short-term rockburst risk evaluation methods can be summarized as precursory characteristics method of time series，fractal theory，machine learning method，probabilistic warning method and empirical method. Different evaluation methods have their own advantages，disadvantages and application conditions. In practice，the evaluation methods can be comprehensively selected based on specific engineering characteristics and existing data.
|
|
|
|
|
|
[1] 李夕兵，周 健，王少锋，等. 深部固体资源开采评述与探索[J]. 中国有色金属学报，2017，27(6)：1 236–1 262.(LI Xibing，ZHOU Jian，WANG Shaofeng，et al. Review and practice of deep mining for solid mineral resources speed[J]. The Chinese Journal of Nonferrous Metals，2017，27(6)：1 236–1 262.(in Chinese))
[2] Ontario Ministry of Labour. Mining health，safety and prevention review[R]. Toronto：Government of Ontario，2015.
[3] COOK N G W. A note on rockbursts considered as a problem of stability[J]. Journal of the Southern African Institute of Mining and Metallurgy，1965，65(8)：437–446.
[4] BLAKE W. Rockburst mechanics[Ph. D. Thesis][D]. Colorado：Colorado School of Mines，1972.
[5] LIANG W Z，SARI A，ZHAO G Y，et al. Short-term rockburst risk prediction using ensemble learning methods[J]. Natural Hazards，2020，104(2)：1 923–1 946.
[6] 郭 立. 深部硬岩岩爆倾向性动态预测模型及其应用[博士学位论文][D]. 长沙：中南大学，2004.(GUO Li. The model to dynamically predict rockbursts proneness of hard rock at depth and its application[Ph. D. Thesis][D]. Changsha：Central South University，2004.(in Chinese))
[7] KIVI I R，AMERI M，MOLLADAVOODI H. Shale brittleness evaluation based on energy balance analysis of stress-strain curves[J]. Journal of Petroleum Science and Engineering，2018，167：1–19.
[8] HAWKES I. Significance of in-situ stress levels[C] // The 1st International Society for Rock Mechanics and Rock Engineering Congress. Lisbon：[s. n.]，1966：3.
[9] TURCHANINOV I A，MARKOV G A，GZOVSKY M V，et al. State of stress in the upper part of the Earth's crust based on direct measurements in mines and on tectonophysical and seismological studies[J]. Physics of the Earth and Planetary Interiors，1972，6(4)：229–234.
[10] RUSSENES B F. Analysis of rock spalling for tunnels in steep valley sides(in Norwegian)[Ph. D. Thesis][D]. Trondheim：Norwegian lnstitute of Technology，1974.
[11] BARTON N，LIEN R，LUNDE J. Engineering classification of rock masses for the design of tunnel support[J]. Rock Mechanics，1974，6(4)：189–236.
[12] BROWN E T，HOEK E. Underground excavations in rock[M]. London：CRC Press，1980：183–243.
[13] Tao Z Y. Support design of tunnels subjected to rockbursting[C]// International Society for Rock Mechanics(ISRM) International Symposium. Madrid：[s. n.]，1988：407–411.
[14] 李燕辉. 对岩爆问题的探讨[J]. 四川水力发电，1990，(3)：24–29.(LI Yanhui. Discussion on rockburst[J]. Sichuan Water Power，1990，(3)：24–29.(in Chinese))
[15] LEE S M，PARK B S，LEE S W. Analysis of rockbursts that have occurred in a waterway tunnel in Korea[J]. International Journal of Rock Mechanics and Mining Sciences，2004，41(Supp.1)：911–916.
[16] 徐林生，王兰生. 二郎山公路隧道岩爆发生规律与岩爆预测研究[J]. 岩土工程学报，1999，21(5)：569–572.(XU Linsheng，WANG Lansheng. Study on the laws of rockburst and its forecasting in the tunnel of Erlang Mountain road[J]. Chinese Journal of Geotechnical Engineering，1999，21(5)：569–572.(in Chinese))
[17] 许梦国，杜子建，姚高辉，等. 程潮铁矿深部开采岩爆预测[J]. 岩石力学与工程学报，2008，27(增1)：2 921–2 928.(XU Mengguo，DU Zijian，YAO Gaohui，et al. Rockburst prediction of Chengchao iron mine during deep mining[J]. Chinese Journal of Rock Mechanics and Engineering，2008，27(Supp.1)：2 921–2 928.(in Chinese))
[18] GOODMAN R E. Introduction to rock mechanics[M]. New York：John Wiley and Sons，Inc.，1980：562.
[19] KIDYBI?SKI A. Bursting liability indices of coal[J]. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts，1981，18(4)：295–304.
[20] KWASNIEWSKI M，SZUTKOWSKI I，WANG J A. Study of ability of coal from seam 510 for storing elastic energy in the aspect of assessment of hazard in Porabka-Klimontow Colliery[R]. Gliwice：Silesian Technical University，1994.
[21] AUBERTIN M，GILL D E，SIMON R. On the use of the brittleness index modified(BIM) to estimate the post-peak behavior of rocks[C]// The 1st North American Rock Mechanics Symposium. [S. l.]：American Rock Mechanics Association，1994：945–952.
[22] 中华人民共和国国家标准编写组. GB50218—94 工程岩体分级标准[S]. 北京：中国计划出版社，1995.(The National Standards Compilation Group of People¢s Republic of China. GB50218—94 Standard for Engineering classification of rock mass[S]. Beijing：China Planning Press，1995.(in Chinese))
[23] 刘小明，李焯芬. 脆性岩石损伤力学分析与岩爆损伤能量指数[J]. 岩石力学与工程学报，1997，16(2)：140–147.(LIU Xiaoming，LEE C F. Damage mechanics analysis for brittle rock and rockburst energy index[J]. Chinese Journal of Rock Mechanics and Engineering，1997，16(2)：140–147.(in Chinese))
[24] 唐礼忠，王文星. 一种新的岩爆倾向性指标[J]. 岩石力学与工程学报，2002，21(6)：874–878.(TANG Lizhong，WANG Wenxing. New rock burst proneness index[J]. Chinese Journal of Rock Mechanics and Engineering，2002，21(6)：874–878.(in Chinese))
[25] LI T B，XIAO X P，SHI Y. Comprehensive integrated methods of rockburst prediction in underground engineering[J]. Advance in Earth Science，2008，23(5)：533–540.
[26] 蔡 朋，邬爱清，汪 斌，等. 一种基于II型全过程曲线的岩爆倾向性指标[J]. 岩石力学与工程学报，2010，29(增1)：3 290–3 294. (CAI Peng，WU Aiqing，WANG Bin，et al. A rockburst proneness index based on class II whole process curve[J]. Chinese Journal of Rock Mechanics and Engineering，2010，29(Supp.1)：3 290–3 294.(in Chinese))
[27] 李夕兵. 岩石动力学基础与应用[M]. 北京：科学出版社，2014：509–510.(LI Xibing. Rock dynamics fundamentals and applications[M]. Beijing：Science Press，2014：509–510.(in Chinese))
[28] 殷志强，李夕兵，董陇军，等. 动静组合加载条件岩爆特性及倾向性指标[J]. 中南大学学报：自然科学版，2014，45(9)：3 249–3 256. (YIN Zhiqiang，LI Xibing，DONG Longjun，et al. Rockburst characteristics and proneness index under coupled static and dynamic loads[J]. Journal of Central South University：Science and Technology，2014，45(9)：3 249–3 256.(in Chinese))
[29] 王超圣，周宏伟，王子辉，等. 不同应力状态下北山花岗岩岩爆倾向性研究[J]. 工程科学与技术，2017，49(6)：84–90.(WANG Chaosheng，ZHOU Hongwei，WANG Zihui，et al. Investigation on the rockburst proneness of Beishan granite under different stress state[J]. Advanced Engineering Sciences，2017，49(6)：84–90.(in Chinese))
[30] 宫凤强，闫景一，李夕兵. 基于线性储能规律和剩余弹性能指数的岩爆倾向性判据[J]. 岩石力学与工程学报，2018，37(9)：1 993– 2 014.(GONG Fengqiang，YAN Jingyi，LI Xibing. A new criterion of rock burst proneness based on the linear energy storage law and the residual elastic energy index[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(9)：1 993–2 014.(in Chinese))
[31] HUCKA V，DAS B. Brittleness determination of rocks by different methods[J]. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts，1974，11(10)：389–392.
[32] 谭以安，孙广忠，郭 志. 岩爆岩石弹射性能综合指数Krb判据[J]. 地质科学，1991，(2)：193–200.(TAN Yian，SUN Guangzhong，GUO Zhi. A composite index Krb criterion for the ejection characteristics of the burst rock[J]. Chinese Journal of Geology，1991，(2)：193–200.(in Chinese))
[33] 彭 祝，王元汉，李廷芥. Griffith理论与岩爆的判别准则[J]. 岩石力学与工程学报，1996，15(增1)：491–495.(PENG Zhu，WANG Yuanhan，LI Tingjie. Griffith theory and the criteria of rockburst[J]. Chinese Journal of Rock Mechanics and Engineering，1996，15(Supp.1)：491–495.(in Chinese))
[34] WU Y K，ZHANG W B. Evaluation of the bursting proneness of coal by means of its failure duration[C]// Proceedings of the 4th International Symposium on Rockbursts and Seismicity in Mines. Rotterdam：[s. n.]，1997：285–288.
[35] HOMAND F，PIGUET J P，REVALOR R. Dynamic phenomena in mines and characteristics of rocks[C]// Proceedings of the 2nd International Symposium on Rock Bursts and Seismicity in Mines. Minneapolis：[s. n.]，1988：195–209.
[36] SIMON R. Analysis of fault-slip mechanisms in hard rock mining[Ph. D. Thesis][D]. Montreal：McGill University，1999.
[37] HEAL D. Observations and analysis of incidences of rockburst damage in underground mines[Ph. D. Thesis][D]. Perth：University of Western Australia，2010.
[38] 邱士利，冯夏庭，张传庆，等. 深埋硬岩隧洞岩爆倾向性指标RVI的建立及验证[J]. 岩石力学与工程学报，2011，30(6)：1 126–1 141. (QIU Shili，FENG Xiating，ZHANG Chuanqing，et al. Development and validation of rockburst vulnerability index(RVI) in deep hard rock tunnels[J]. Chinese Journal of Rock Mechanics and Engineering，2011，30(6)：1 126–1 141.(in Chinese))
[39] 尚彦军，张镜剑，傅冰骏. 应变型岩爆三要素分析及岩爆势表达[J]. 岩石力学与工程学报，2013，32(8)：1 520–1 527.(SHANG Yanjun，ZHANG Jingjian，FU Bingjun. Analyses of three parameters for strain mode rockburst and expression of rockburst potential[J]. Chinese Journal of Rock Mechanics and Engineering，2013，32(8)：1 520– 1 527.(in Chinese))
[40] 郭建强，赵 青，王军保，等. 基于弹性应变能岩爆倾向性评价方法研究[J]. 岩石力学与工程学报，2015，34(9)：1 886–1 893.(GUO Jianqiang，ZHAO Qing，WANG Junbao，et al. Rockburst prediction based on elastic strain energy[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(9)：1 886–1 893.(in Chinese))
[41] 张传庆，俞 缙，陈 珺，等. 地下工程围岩潜在岩爆问题评估方法[J]. 岩土力学，2016，37(增1)：341–349.(ZHANG Chuanqing，YU Jin，CHEN Jun，et al. Evaluation method for potential rockburst in underground engineering[J]. Rock and Soil Mechanics，2016，37(Supp.1)：341–349.(in Chinese))
[42] 王元汉，李卧东，李启光，等. 岩爆预测的模糊数学综合评判方法[J]. 岩石力学与工程学报，1998，17(5)：493–501.(WANG Yuanhan，LI Wodong，LEE P K K，et al. Method of fuzzy comprehensive evaluations for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering，1998，17(5)：493–501.(in Chinese))
[43] 姜 彤，黄志全，赵彦彦. 动态权重灰色归类模型在南水北调西线工程岩爆风险评估中的应用[J]. 岩石力学与工程学报，2004，23(7)：1 104–1 108.(JIANG Tong，HUANG Zhiquan，ZHAO Yanyan. Dynamically weighted grey optimization model for rockburst risk forecasting and its application to western route of south-north water transfer project[J]. Chinese Journal of Rock Mechanics and Engineering，2004，23(7)：1 104–1 108.(in Chinese))
[44] 文畅平. 属性综合评价系统在岩爆发生和烈度分级中的应用[J]. 工程力学，2008，25(6)：153–158.(WEN Changping. Application of attribute synthetic evaluation system in prediction of possibility and classification of rockburst[J]. Engineering Mechanics，2008，25(6)：153–158.(in Chinese))
[45] WANG M，JIN J，LI L. SPA-VFS model for the prediction of rockburst[C] //The 5th International Conference on Fuzzy Systems and Knowledge Discovery. [S. l.]：The Institute of Electrical and Electronics Engineers(IEEE)，2008：34‐38.
[46] 王迎超，尚岳全，孙红月，等. 基于功效系数法的岩爆烈度分级预测研究[J]. 岩土力学，2010，31(2)：529–534.(WANG Yingchao，SHANG Yuequan，SUN Hongyue，et al. Study of prediction of rockburst intensity based on efficacy coefficient method[J]. Rock and Soil Mechanics，2010，31(2)：529–534.(in Chinese))
[47] 胡建华，尚俊龙，周科平. 岩爆烈度预测的改进物元可拓模型与实例分析[J]. 中国有色金属学报，2013，23(2)：495–502.(HU Jianhua，SHANG Junlong，ZHOU Keping. Improved matter-element extension model and its application to prediction of rockburst intensity[J]. The Chinese Journal of Nonferrous Metals，2013，23(2)：495–502.(in Chinese))
[48] 贾义鹏，吕 庆，尚岳全，等. 基于证据理论的岩爆预测[J]. 岩土工程学报，2014，36(6)：1 079–1 086.(JIA Yipeng，LÜ Qing，SHANG Yuequan，et al. Rockburst prediction based on evidence theory[J]. Chinese Journal of Geotechnical Engineering，2014，36(6)：1 079–1 086.(in Chinese))
[49] 裴启涛，李海波，刘亚群，等. 基于组合赋权的岩爆倾向性预测灰评估模型及应用[J]. 岩土力学，2014，35(增1)：49–56.(PEI Qitao，LI Haibo，LIU Yaqun，et al. A grey evaluation model for predicting rockburst proneness based on combination weight and its application[J]. Rock and Soil Mechanics，2014，35(Supp.1)：49–56.(in Chinese))
[50] 邬书良，陈建宏. 约简概念格的粗糙集在岩爆烈度判别中的应用[J]. 岩石力学与工程学报，2014，33(10)：2 125–2 131.(WU Shuliang，CHEN Jianhong. Application of rough set theory to rockburst intensity prediction based on reduced concept lattice[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(10)：2 125–2 131.(in Chinese))
[51] 刘磊磊，张绍和，王晓密，等. 变权靶心贴近度在岩爆烈度预测中的应用[J]. 爆炸与冲击，2015，35(1)：43–50.(LIU Leilei，ZHANG Shaohe，WANG Xiaomi，et al. Application of target approaching with variable weight in prediction of rockburst intensity[J]. Explosion and Shock Waves，2015，35(1)：43–50.(in Chinese))
[52] WANG C L，WU A X，LU H，et al. Predicting rockburst tendency based on fuzzy matter-element model[J]. International Journal of Rock Mechanics and Mining Sciences，2015，75：224–232.
[53] ZHOU K P，LIN Y，DENG H W，et al. Prediction of rock burst classification using cloud model with entropy weight[J]. Transactions of Nonferrous Metals Society of China，2016，26(7)：1 995–2 002.
[54] PU Y Y，APEL D，XU H W. A principal component analysis/fuzzy comprehensive evaluation for rockburst potential in kimberlite[J]. Pure and Applied Geophysics，2018，175(6)：2 141–2 151.
[55] 张天余，李建朋，廖万辉. 突变级数法在隧道岩爆等级预测中应用[J]. 公路，2018，(9)：316–320.(ZHANG Tianyu，LI Jianpeng，LIAO Wanhui. Application of catastrophe progression method in prediction of tunnel rockburst grade[J]. Highway，2018，(9)：316–320.(in Chinese))
[56] 过 江，张为星，赵 岩. 岩爆预测的多维云模型综合评判方法[J]. 岩石力学与工程学报，2018，37(5)：1 199–1 206.(GUO Jiang，ZHANG Weixing，ZHAO Yan. A multidimensional cloud model for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(5)：1 199–1 206.(in Chinese))
[57] LIU R，YE Y C，HU N Y，et al. Classified prediction model of rockburst using rough sets-normal cloud[J]. Neural Computing and Applications，2019，31(12)：8 185–8 193.
[58] XUE Y G，LI Z Q，LI S C，et al. Prediction of rock burst in underground caverns based on rough set and extensible comprehensive evaluation[J]. Bulletin of Engineering Geology and the Environment，2019，78(1)：417–429.
[59] WANG X T，LI S C，XU Z H，et al. An interval fuzzy comprehensive assessment method for rock burst in underground caverns and its engineering application[J]. Bulletin of Engineering Geology and the Environment，2019，78(7)：5 161–5176.
[60] JIA Q J，WU L，LI B，et al. The comprehensive prediction model of rockburst tendency in tunnel based on optimized unascertained measure theory[J]. Geotechnical and Geological Engineering，2019，37(4)：3 399–3 411.
[61] WANG M W，LIU Q Y，WANG X，et al. Prediction of rockburst based on multidimensional connection cloud model and set pair analysis[J]. International Journal of Geomechanics，2020，20(1)：04019147.
[62] 周科平，雷 涛，胡建华. 深部金属矿山RS-TOPSIS岩爆预测模型及其应用[J]. 岩石力学与工程学报，2013，32(增2)：3 705–3 711. (ZHOU Keping，LEI Tao，HU Jianhua. RS-TOPSIS model of rockburst prediction in deep metal mines and its application[J]. Chinese Journal of Rock Mechanics and Engineering，2013，32(Supp.2)：3 705–3 711. (in Chinese))
[63] 左 蕾，章求才，刘玉龙，等. 岩爆倾向性分析的CW-GT-TODIM预测模型及其应用[J]. 世界科技研究与发展，2016，38(6)：1 131– 1 136.(ZUO Lei，ZHANG Qiucai，LIU Yulong，et al. Predication model of CW-GT-TODIM for rockburst tendency analysis and its application[J]. World Sci-Tech R&D，2016，38(6)：1 131–1 136.(in Chinese))
[64] 徐 琛，刘晓丽，王恩志，等. 基于组合权重–理想点法的应变型岩爆五因素预测分级[J]. 岩土工程学报，2017，39(12)：2 245–2 252. (XU Chen，LIU Xiaoli，WANG Enzhi，et al. Prediction and classification of strain mode rockburst based on five-factor criterion and combined weight-ideal point method[J]. Chinese Journal of Geotechnical Engineering，2017，39(12)：2 245–2 252.(in Chinese))
[65] XU C，LIU X L，WANG E Z，et al. Rockburst prediction and classification based on the ideal-point method of information theory[J]. Tunnelling and Underground Space Technology，2018，81：382–390.
[66] 赵国彦，李振阳，梁伟章，等. 岩爆预测的Vague集模型[J]. 矿冶工程，2018，38(1)：1–4.(ZHAO Guoyan，LI Zhenyang，LIANG Weizhang，et al. Vague set model for rockburst prediction[J]. Mining and Metallurgical Engineering，2018，38(1)：1–4.(in Chinese))
[67] LIANG W Z，ZHAO G Y，WU H，et al. Risk assessment of rockburst via an extended MABAC method under fuzzy environment[J]. Tunnelling and Underground Space Technology，2019，83：533–544.
[68] XUE Y G，BAI C H，KONG F M，et al. A two-step comprehensive evaluation model for rockburst prediction based on multiple empirical criteria[J]. Engineering Geology，2020，268：105515.
[69] FENG X T，WANG L N. Rockburst prediction based on neural networks[J]. Transactions of Nonferrous Metals Society of China，1994，4(1)：7–14.
[70] 朱宝龙，陈 强，胡厚田. 基于人工神经网络的岩爆预测方法[J]. 地质灾害与环境保护，2002，13(3)：56–59.(ZHU Baolong，CHEN Qiang，HU Houtian. Rockburst prediction method based on the artifical neural network[J]. Journal of Geological Hazards and Environment Preservation，2002，13(3)：56–59.(in Chinese))
[71] 陈海军，郦能惠，聂德新，等. 岩爆预测的人工神经网络模型[J]. 岩土工程学报，2002，24(2)：229–232.(CHEN Haijun，LI Nenghui，NIE Dexin，et al. A model for prediction of rockburst by artificial neural network[J]. Chinese Journal of Geotechnical Engineering，2002，24(2)：229–232.(in Chinese))
[72] 丁向东，吴继敏，李 健，等. 岩爆分类的人工神经网络预测方法[J]. 河海大学学报：自然科学版，2003，31(4)：424–427.(DING Xiangdong，WU Jimin，LI Jian，et al. Artificial neural network for forecasting and classification of rockbursts[J]. Journal of Hohai University：Natural Sciences，2003，31(4)：424–427.(in Chinese))
[73] 赵洪波. 岩爆分类的支持向量机方法[J]. 岩土力学，2005，26(4)：642–644.(ZHAO Hongbo. Classification of rockburst using support vector machine[J]. Rock and Soil Mechanics，2005，26(4)：642–644.(in Chinese))
[74] 郭 雷，李夕兵，岩小明，等. 基于 BP 网络理论的岩爆预测方法[J]. 工业安全与环保，2005，31(10)：32–35.(GUO Lei，LI Xibing，YAN Xiaoming，et al. Rock burst prediction methods based on BP network theory[J]. Industrial Safety and Environmental Protection，2005，31(10)：32–35.(in Chinese))
[75] 李俊宏，姜弘道. 基于支持向量机的岩爆识别模型[J]. 水利学报，2007，(增1)：667–670.(LI Junhong，JIANG Hongdao. Model of rockburst identification based on SVM[J]. Journal of Hydraulic Engineering，2007，(Supp.1)：667–670.(in Chinese))
[76] 葛启发，冯夏庭. 基于AdaBoost组合学习方法的岩爆分类预测研究[J]. 岩土力学，2008，29(4)：943–948.(GE Qifa，FENG Xiating. Classification and prediction of rockburst using AdaBoost combination learning method[J]. Rock and Soil Mechanics，2008，29(4)：943–948.(in Chinese))
[77] 祝云华，刘新荣，周军平. 基于v-SVR算法的岩爆预测分析[J]. 煤炭学报，2008，33(3)：277–281.(ZHU Yunhua，LIU Xinrong，ZHOU Junping. Rockburst prediction analysis based on v-SVR algorithm[J]. Journal of China Coal Society，2008，33(3)：277–281.(in Chinese))
[78] SU G S，ZHANG K S，CHEN Z. Rockburst prediction using Gaussian process machine learning[C]// International Conference on Computational Intelligence and Software Engineering，IEEE. [S. l.]：[s. n.]，2009：1–4.
[79] 白云飞，邓 建，董陇军，等. 深部硬岩岩爆预测的FDA模型及其应用[J]. 中南大学学报：自然科学版，2009，40(5)：1 417–1 422. (BAI Yunfei，DENG Jian，DONG Longjun，et al. Fisher discriminant analysis model of rock burst prediction and its application in deep hard rock engineering[J]. Journal of Central South University：Science and Technology，2009，40(5)：1 417–1 422.(in Chinese))
[80] 宫凤强，李夕兵，张 伟. 基于Bayes判别分析方法的地下工程岩爆发生及烈度分级预测[J]. 岩土力学，2010，31(增1)：370–377.(GONG Fengqiang，LI Xibing，ZHANG Wei. Rockburst prediction of underground engineering based on Bayes discriminant analysis method[J]. Rock and Soil Mechanics，2010，31(Supp.1)：370–377.(in Chinese))
[81] ZHOU J，LI X B，SHI X Z. Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines[J]. Safety Science，2012，50(4)：629–644.
[82] 张乐文，张德永，李术才，等. 基于粗糙集理论的遗传‐RBF神经网络在岩爆预测中的应用[J]. 岩土力学，2012，33(增1)：270–276.(ZHANG Lewen，ZHANG Deyong，LI Shucai，et al. Application of RBF neural network to rockburst prediction based on rough set theory[J]. Rock and Soil Mechanics，2012，33(Supp.1)：270–276.(in Chinese))
[83] DONG L J，LI X B，PENG K. Prediction of rockburst classification using Random Forest[J]. Transactions of Nonferrous Metals Society of China，2013，23(2)：472–477.
[84] 贾义鹏，吕 庆，尚岳全. 基于粒子群算法和广义回归神经网络的岩爆预测[J]. 岩石力学与工程学报，2013，32(2)：343–348.(JIA Yipeng，LU Qing，SHANG Yuequan. Rockburst prediction using particle swarm optimization algorithm and general regression neural network[J]. Chinese Journal of Rock Mechanics and Engineering，2013，32(2)：343–348.(in Chinese))
[85] 兰 明，刘志祥，冯 凡. 在线极限学习机在岩爆预测中的应用[J]. 安全与环境学报，2014，14(2)：90–93.(LAN Ming，LIU Zhixiang，FENG Fan. Attempt to study the applicability of the online sequential extreme learning machine to the rock burst forecast[J]. Journal of Safety and Environment，2014，14(2)：90–93.(in Chinese))
[86] GAO W. Forecasting of rockbursts in deep underground engineering based on abstraction ant colony clustering algorithm[J]. Natural Hazards，2015，76(3)：1 625–1 649.
[87] 邱道宏，李术才，张乐文，等. 基于模型可靠性检查的QGA-SVM岩爆倾向性分类研究[J]. 应用基础与工程科学学报，2015，23(5)：981–991.(QIU Daohong，LI Shucai，ZHANG Lewen，et al. Research on QGA-SVM rock burst orientation classification based on model reliability examination[J]. Journal of Basic Science and Engineering，2015，23(5)：981–991.(in Chinese))
[88] JIANG K，LU J，XIA K L. A novel algorithm for imbalance data classification based on genetic algorithm improved SMOTE[J]. Arabian journal for science and engineering，2016，41(8)：3 255–3 266.
[89] ZHOU J，LI X B，MITRI H S. Classification of rockburst in underground projects：comparison of ten supervised learning methods[J]. Journal of Computing in Civil Engineering，2016，30(5)：04016003.
[90] LI N，FENG X D，JIMENEZ R. Predicting rock burst hazard with incomplete data using Bayesian networks[J]. Tunnelling and Underground Space Technology，2017，61：61–70.
[91] LI T Z，LI Y X，YANG X L. Rock burst prediction based on genetic algorithms and extreme learning machine[J]. Journal of Central South University，2017，24(9)：2 105–2 113.
[92] LI N，JIMENEZ R. A logistic regression classifier for long-term probabilistic prediction of rock burst hazard[J]. Natural Hazards，2018，90(1)：197–215.
[93] AFRAEI S，SHAHRIAR K，MADANI S H. Statistical assessment of rock burst potential and contributions of considered predictor variables in the task[J]. Tunnelling and Underground Space Technology，2018，72：250–271.
[94] PU Y Y，APEL D B，WANG C，et al. Evaluation of burst liability in kimberlite using support vector machine[J]. Acta Geophysica，2018，66(5)：973–982.
[95] PU Y Y，APEL D B，LINGGA B. Rockburst prediction in kimberlite using decision tree with incomplete data[J]. Journal of Sustainable Mining，2018，17(3)：158–165.
[96] LIN Y，ZHOU K P，LI J L. Application of cloud model in rock burst prediction and performance comparison with three machine learning algorithms[J]. IEEE Access，2018，6：30 958–30 968.
[97] 邵良杉，周 玉. 基于MIV-MA-KELM模型的岩爆烈度等级预测[J]. 中国安全科学学报，2018，28(2)：34–39.(SHAO Liangshan，ZHOU Yu. MIV-MA-KELM model based prediction of rockburst intensity grade[J]. China Safety Science Journal，2018，28(2)：34–39.(in Chinese))
[98] 徐 佳，陈俊智，刘晨毓，等. DHNN模型在岩爆烈度分级预测中的应用研究[J]. 工矿自动化，2018，44(1)：84–88.(XU Jia，CHEN Junzhi，LIU Chenyu，et al. Application research of DHNN model in prediction of classification of rockburst intensity[J]. Industry and Mine Automation，2018，44(1)：84–88.(in Chinese))
[99] FARADONBEH R S，TAHERI A. Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques[J]. Engineering with Computers，2019，35(2)：659–675.
[100] PU Y Y，APEL D B，POURRAHIMIAN Y，et al. Evaluation of rockburst potential in kimberlite using fruit fly optimization algorithm and generalized regression neural networks[J]. Archives of Mining Sciences，2019，64(2)：279–296.
[101] WU S C，WU Z G，ZHANG C X. Rock burst prediction probability model based on case analysis[J]. Tunnelling and Underground Space Technology，2019，93：103069.
[102] PU Y Y，APEL D B，XU H. Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier[J]. Tunnelling and Underground Space Technology，2019，90：12–18.
[103] FARADONBEH R S，HAGHSHENAS S S，TAHERI A，et al. Application of self-organizing map and fuzzy c-mean techniques for rockburst clustering in deep underground projects[J]. Neural Computing and Applications，2020，32(12)：8 545–8 559.
[104] PU Y Y，APEL D B，WEI C. Applying machine learning approaches to evaluating rockburst liability：a comparation of generative and discriminative models[J]. Pure and Applied Geophysics，2019，176(10)：4 503–4 517.
[105] ZHENG Y C，ZHONG H，FANG Y，et al. Rockburst prediction model based on entropy weight integrated with grey relational BP neural network[J]. Advances in Civil Engineering，2019，2019：3453614.
[106] 吴顺川，张晨曦，成子桥. 基于PCA-PNN原理的岩爆烈度分级预测方法[J]. 煤炭学报，2019，44(9)：2 767–2 776.(WU Shunchuan，ZHANG Chenxi，CHENG Ziqiao. Prediction of intensity classification of rockburst based on PCA-PNN principle[J]. Journal of China Coal Society，2019，44(9)：2 767–2 776.(in Chinese))
[107] 赵国彦，刘雷磊，王剑波，等. 岩爆等级预测的PCA-OPF模型[J]. 矿冶工程，2019，39(4)：1–5.(ZHAO Guoyan，LIU Leilei，WANG Jianbo，et al. PCA-OPF model for rock burst prediction[J]. Mining and Metallurgical Engineering，2019，39(4)：1–5.(in Chinese))
[108] 刘志祥，郑 斌，刘 进，等. 金属矿深部开采岩爆危险预测的 GA-ELM 模型研究[J]. 矿冶工程，2019，39(3)：1–4.(LIU Zhixiang，ZHENG Bin，LIU Jin，et al. Rockburst prediction with GA-ELM model for deep mining of metal mines[J]. Mining and Metallurgical Engineering，2019，39(3)：1–4.(in Chinese))
[109] GHASEMI E，GHOLIZADEH H，ADOKO A C. Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach[J]. Engineering with Computers，2020，36(1)：213–225.
[110] ZHOU J，GUO H Q，KOOPIALIPOOR M，et al. Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm[J/OL]. Engineering with Computers，https：//doi.org/10.1007/s00366–019–00908–9.
[111] XUE Y G，BAI C H，QIU D H，et al. Predicting rockburst with database using particle swarm optimization and extreme learning machine[J]. Tunnelling and Underground Space Technology，2020，98：103287.
[112] 谢学斌，李德玄，孔令燕，等. 基于CRITIC-XGB算法的岩爆倾向等级预测模型[J]. 岩石力学与工程学报，2020，39(10)：1 975– 1 982.(XIE Xuebin，LI Dexuan，KONG Lingyan，et al. Rockburst propensity prediction model based on CRITIC-XGB algorithm[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(10)：1 975–1 982.(in Chinese))
[113] 田 睿，孟海东，陈世江，等. 基于深度神经网络的岩爆烈度分级预测研究[J/OL]. 煤炭学报，https：//doi.org/10.13225/ j.cnki.jccs. 2019.1763.(TIAN Rui，MENG Haidong，CHEN Shijiang，et al. Prediction of intensity classification of rockburst based on deep neural network[J/OL]. Journal of China Coal Society，https：//doi.org/10.13225/ j.cnki.jccs.2019.1763.(in Chinese))
[114] 汤志立，徐千军. 基于9种机器学习算法的岩爆预测研究[J]. 岩石力学与工程学报，2020，39(4)：773–781.(TANG Zhili，XU Qianjun. Rockburst prediction based on nine machine learning algorithms[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(4)：773–781.(in Chinese))
[115] COOK N G W. The design of underground excavations[C]// Proceedings of the 8th Rock Mechanics Symposium. Minnesota：American Rock Mechanics Association，1966：45–52.
[116] RYDER J A. Excess shear stress in the assessment of geologically hazardous situations[J]. Journal of the Southern African Institute of Mining and Metallurgy，1988，88(1)：27–39.
[117] WILES T D，MARISETT S D，MARTIN C D. Correlation between local energy release density and observed bursting conditions at Creighton mine[R]. Sudbury：Mine Modelling Ltd.，1998.
[118] MITRI H S，TANG B，SIMON R. FE modelling of mining-induced energy release and storage rates[J]. Journal of the Southern African Institute of Mining and Metallurgy，1999，99(2)：103–110.
[119] 苏国韶，冯夏庭，江 权，等. 高地应力下地下工程稳定性分析与优化的局部能量释放率新指标研究[J]. 岩石力学与工程学报，2006，25(12)：2 453–2 460.(SU Guoshao，FENG Xiating，JIANG Quan，et al. Study on new index of local energy release rate for stability analysis and optimal design of underground rockmass engineering with high geostress[J]. Chinese Journal of Rock Mechanics and Engineering，2006，25(12)：2 453–2 460.(in Chinese))
[120] 陈卫忠，吕森鹏，郭小红，等. 基于能量原理的卸围压试验与岩爆判据研究[J]. 岩石力学与工程学报，2009，28(8)：1 530–1 540. (CHEN Weizhong，LU Senpeng，GUO Xiaohong，et al. Research on unloading confining pressure tests and rockburst criterion based on energy theory[J]. Chinese Journal of Rock Mechanics and Engineering，2009，28(8)：1 530–1 540.(in Chinese))
[121] ZHANG C Q，ZHOU H，FENG X T. An index for estimating the stability of brittle surrounding rock mass：FAI and its engineering application[J]. Rock Mechanics and Rock Engineering，2011，44(4)：401–414.
[122] CASTRO L M，BEWICK R，CARTER T G. An overview of numerical modelling applied to deep mining[C]// Innovative Numerical Modelling in Geomechanics. London：CRC Press，2012：393–414.
[123] 邱士利，冯夏庭，江 权，等. 深埋隧洞应变型岩爆倾向性评估的新数值指标研究[J]. 岩石力学与工程学报，2014，33(10)：2 007– 2 017.(QIU Shili，FENG Xiating，JIANG Quan，et al. A novel numerical index for estimating strainburst vulnerability in deep tunnels[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(10)：2 007–2 017.(in Chinese))
[124] 杨凡杰，周 辉，卢景景，等. 岩爆发生过程的能量判别指标[J]. 岩石力学与工程学报，2015，34(增1)：2 706–2 714.(YANG Fanjie，ZHOU Hui，LU Jingjing，et al. An energy criterion in process of rockburst[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(Supp.1)：2 706–2 714.(in Chinese))
[125] CAI M F. Prediction and prevention of rockburst in metal mines—A case study of Sanshandao gold mine[J]. Journal of Rock Mechanics and Geotechnical Engineering，2016，8(2)：204–211.
[126] XU J，JIANG J，XU N，et al. A new energy index for evaluating the tendency of rockburst and its engineering application[J]. Engineering Geology，2017，230：46–54.
[127] XUE Y，CAO Z Z，DU F，et al. The influence of the backfilling roadway driving sequence on the rockburst risk of a coal pillar based on an energy density criterion[J]. Sustainability，2018，10：2 609.
[128] 郑颖人，刘兴华. 近代非线性科学与岩石力学问题[J]. 岩土工程学报，1996，18(1)：98–100.(ZHENG Yingren，LIU Xinghua. Modern nonlinear science and rock mechanics problems[J]. Chinese Journal of Geotechnical Engineering，1996，18(1)：98–100.(in Chinese))
[129] 潘一山，章梦涛，李国臻. 洞室岩爆的尖角突变模型[J]. 应用数学和力学，1994，15(10)：893–900.(PAN Yishan，ZHANG Mengtao，LI Guozheng. The study of chamber rockburst by the CUSP model of catastrophe theory[J]. Applied Mathematics and Mechanics，1994，15(10)：893–900.(in Chinese))
[130] 费鸿禄，徐小荷，唐春安. 地下硐室岩爆的突变理论研究[J]. 煤炭学报，1995，20(1)：29–33.(FEI Honglu，XU Xiaohe，TANG Chun¢an. Research on theory of catastrophe of rock burst in underground chamber[J]. Journal of China Coal Society，1995，20(1)：29–33.(in Chinese))
[131] 单晓云，徐东强，张艳博. 用突变理论预报巷道岩爆发生的可能性[J]. 矿山测量，2000，(4)：36–37.(SHAN Xiaoyun，XU Dongqiang，ZHANG Yanbo. Predicting the possibility of tunnel rockburst using catastrophe theory[J]. Mine Surveying，2000，(4)：36–37.(in Chinese))
[132] 左宇军，李夕兵，赵国彦. 洞室层裂屈曲岩爆的突变模型[J]. 中南大学学报：自然科学版，2005，36(2)：311–316.(ZUO Yujun，LI Xibing，ZHAO Guoyan. A catastrophe model for underground chamber rock burst under lamination spallation bucking[J]. Journal of Central South University：Science and Technology，2005，36(2)：311–316.(in Chinese))
[133] 潘 岳，张 勇，于广明. 圆形硐室岩爆机制及其突变理论分析[J]. 应用数学和力学，2006，27(6)：741–749.(PAN Yue，ZHANG Yong，YU Guangming. Mechanism and catastrophe theory analysis of circular tunnel rockburst[J]. Applied Mathematics and Mechanics，2006，27(6)：741–749.(in Chinese))
[134] WANG S Y，LAM K C，AU S K，et al. Analytical and numerical study on the pillar rockbursts mechanism[J]. Rock Mechanics and Rock Engineering，2006，39(5)：445–467.
[135] 李长洪，张立新，张 磊，等. 灰色突变理论及声发射在岩爆预测中的应用[J]. 中国矿业，2008，7(8)：87–90.(LI Changhong，ZHANG Lixin，ZHANG Lei，et al. Application of grey catastrophe theory and acoustic emission in rock burst prediction[J]. China Mining Magazine，2008，7(8)：87–90.(in Chinese))
[136] SHAN Q Y，QIN T. The improved drilling cutting method and its engineering applications[J]. Geotechnical and Geological Engineering，2019，37(5)：3 715–3 726.
[137] FRID V，VOZOFF K. Electromagnetic radiation induced by mining rock failure[J]. International Journal of Coal Geology，2005，64(1/2)：57–65.
[138] 吴其斌. 微重力方法在岩爆预测中的应用[J]. 地球物理学进展，1993，8(3)：136–142.(WU Qibin. Application of microgravity method in rockburst prediction[J]. Progress in Geophysics，1993，8(3)：136–142.(in Chinese))
[139] AYDAN Ö，MALISTANI N，TOKASHIKI N. The possibility of infrared camera thermography for assessing the real-time stability of tunnels against rockburst[C]// The 51st US Rock Mechanics/ Geomechanics Symposium，American Rock Mechanics Association. [S. l.]：[s. n.]，2017：ARMA–2017–0479.
[140] HIRATA A，KAMEOKA Y，HIRANO T. Safety management based on detection of possible rock bursts by AE monitoring during tunnel excavation[J]. Rock Mechanics and Rock Engineering，2007，40(6)：563–576.
[141] XIAO Y X，FENG X T，HUDSON J A，et al. ISRM suggested method for in situ microseismic monitoring of the fracturing process in rock masses[J]. Rock Mechanics and Rock Engineering，2016，49(1)：343–369.
[142] HUDYMA M，POTVIN Y H. An engineering approach to seismic risk management in hardrock mines[J]. Rock Mechanics and Rock Engineering，2010，43(6)：891–906.
[143] DURRHEIM R J，CICHOWICZ A，EBRAHIM-TROLLOPE R，et al. Guidelines，standards and best practice for seismic hazard assessment and rockburst risk management in South African mines[C]// Proceedings of the Fourth International Seminar on Deep and High Stress Mining，Nedlands：Australian Centre for Geomechanics，2007：249–261
[144] 冯夏庭，陈炳瑞，张传庆，等. 岩爆孕育过程的机制、预警与动态调控[M]. 北京：科学出版社，2013：394–434.(FENG Xiating，CHEN Bingrui，ZHANG Chuanqing，et al. Mechanism，warning and dynamic control of rockburst development processes[M]. Beijing：Science Press，2013：394–434(in Chinese))
[145] CAI W，DOU L M，ZHANG M，et al. A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring[J]. Tunnelling and Underground Space Technology，2018，80：232–245.
[146] BRADY B T，LEIGHTON F W. Seismicity anomaly prior to a moderate rock burst：A case study[J]. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts，1977，14(3)：127–132.
[147] MENDECKI A J，GIBOWICZ S J，LASOCKI S. Principles of monitoring seismic rockmass response to mining[C]// Rockbursts and Seismicity in Mines. Rotterdam：Balkema，1997：69–79.
[148] ALCOTT J M，KAISER P K，SIMSER B P. Use of microseismic source parameters for rockburst hazard assessment[J]. Pure and Applied Geophysics，1998，153：41–65.
[149] TANG L Z，XIA K W. Seismological method for prediction of areal rockbursts in deep mine with seismic source mechanism and unstable failure theory[J]. Journal of Central South University of Technology，2010，17(5)：94 |
|
|
|
2022, Vol. 41 
