Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation

LIU Weijun; FAN Junqi; LI Tianbin; GUO Peng; ZENG Peng; JU Guanghong

doi:10.16030/j.cnki.issn.1000-3665.202111027

2022 Vol. 49, No. 6

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Article navigation > Hydrogeology & Engineering Geology > 2022 > 49(6): 114-123

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LIU Weijun, FAN Junqi, LI Tianbin, GUO Peng, ZENG Peng, JU Guanghong. Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation[J]. Hydrogeology & Engineering Geology, 2022, 49(6): 114-123. doi: 10.16030/j.cnki.issn.1000-3665.202111027

Citation:

LIU Weijun, FAN Junqi, LI Tianbin, GUO Peng, ZENG Peng, JU Guanghong. Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation[J]. Hydrogeology & Engineering Geology, 2022, 49(6): 114-123. doi: 10.16030/j.cnki.issn.1000-3665.202111027

Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation

1.
College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, Sichuan　610059, China
2.
Northwest Engineering Corporation Limited, Xi’an, Shaanxi　710000, China
3.
Institute of Defense Engineering, Academy of Military Sciences, Luoyang, Henan　471023, China

More Information

Corresponding author: FAN Junqi, lyfjq@163.com

Received Date: 12 November 2021

Revised Date: 10 March 2022

Publish Date: 15 November 2022

Abstract

Abstract

Rockburst is a dynamic process of a sudden and rapid release of elastic strain energy stored in hard rock mass during underground excavation. The occurrence of rockburst disaster during tunnel construction will cause serious consequences such as casualties, equipment damage and construction delay. With a large number of deep-buried long tunnels to be constructed in southwestern mountainous areas of China, the prediction of rockburst disaster is of great importance. In this paper, to fulfil the requirement of tunnel alignment and design during engineering investigation stage, on the premise of the availability of rockburst prediction indexes in this stage, the Bayesian network is used to reflect the relationship between rockburst intensity and various influencing factors. Based on 473 groups of rockburst cases, the naive Bayesian probability classification model is constructed to predict the rockburst intensity by using four prediction indexes—geo-stress, geological structure, surrounding rock grade and surrounding rock strength. The prediction accuracy of the model is found to be 84.47% using the 10-fold cross validation method. At the same time, this model is applied to the rockburst section of Paomashan No. 1 Tunnel of Ya’an—Yecheng Expressway. The results show that the prediction accuracy is 85.71% in the 28 tunnel section applications, and the established Bayesian network model has a good prediction performance. The proposed method can provide a good support to the rockburst prediction during the investigation of deep-buried long tunnels located in Southwest China.
- deep-buried hard rock tunnel /
- investigation stage /
- rockburst disaster /
- probabilistic classification prediction /
- Bayesian network

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References

[1]	贾金晓. 米仓山隧道岩爆综合集成预测及处治措施研究[D]. 成都: 成都理工大学, 2016 Google Scholar JIA Jinxiao. Study of meta-synthesis prediction methods and treatment measures on rock burst in the Micangshan tunnel[D]. Chengdu: Chengdu University of Technology, 2016. （in Chinese with English abstract） Google Scholar
[2]	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. doi: 10.1016/j.ssci.2011.08.065 CrossRef Google Scholar
[3]	陈仕阔,李涵睿,周航,等. 基于岩爆危险性评价的川藏铁路某深埋硬岩隧道线路方案比选研究[J]. 水文地质工程地质,2021,48(5):81 − 90. [CHEN Shikuo,LI Hanrui,ZHOU Hang,et al. Route selection of deep-lying and hard rock tunnel in the Sichuan-Tibet Railway based on rock burst risk assessment[J]. Hydrogeology & Engineering Geology,2021,48(5):81 − 90. (in Chinese with English abstract) doi: 10.16030/j.cnki.issn.1000-3665.202103099 CrossRef Google Scholar
[4]	唐杰灵,李天斌,曾鹏,等. 岩爆柔性防护网及其动力特性分析[J]. 岩石力学与工程学报,2019,38(4):793 − 802. [TANG Jieling,LI Tianbin,ZENG Peng,et al. Study on rockburst flexible protective net and its dynamic characteristics[J]. Chinese Journal of Rock Mechanics and Engineering,2019,38(4):793 − 802. (in Chinese with English abstract) Google Scholar
[5]	RUSSENES B F. Analysis of rock spalling for tunnels in steep valley sides(in Norwegian) [R]. Trondheim: Norwegian Institute of Technology, 1974. Google Scholar
[6]	KIDYBIŃSKI A. Bursting liability indices of coal[J]. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts,1981,18(4):295 − 304. Google Scholar
[7]	张镜剑,傅冰骏. 岩爆及其判据和防治[J]. 岩石力学与工程学报,2008,27(10):2034 − 2042. [ZHANG Jingjian,FU Bingjun. Rockburst and its criteria and control[J]. Chinese Journal of Rock Mechanics and Engineering,2008,27(10):2034 − 2042. (in Chinese with English abstract) doi: 10.3321/j.issn:1000-6915.2008.10.010 CrossRef Google Scholar
[8]	徐林生,王兰生,李天斌,等. 二郎山公路隧道岩爆特征与预测研究[J]. 地质灾害与环境保护,1999,10(2):55 − 59. [XU Linsheng,WANG Lansheng,LI Tianbin,et al. Study on the character of rockburst and its forecasting in the Erlang mountain tunnel[J]. Journal of Geological Hazards and Environment Preservation,1999,10(2):55 − 59. (in Chinese with English abstract) doi: 10.3969/j.issn.1006-4362.1999.02.010 CrossRef Google Scholar
[9]	冯夏庭. 地下峒室岩爆预报的自适应模式识别方法[J]. 东北大学学报,1994,15(5):471 − 475. [FENG Xiating. Adaptive pattern recognition to predict rockbursts in underground openings[J]. Journal of Northeastern University,1994,15(5):471 − 475. (in Chinese with English abstract) Google Scholar
[10]	冯夏庭,赵洪波. 岩爆预测的支持向量机[J]. 东北大学学报,2002,23(1):57 − 59. [FENG Xiating,ZHAO Hongbo. Prediction of rockburst using support vector machine[J]. Journal of Northeastern University,2002,23(1):57 − 59. (in Chinese with English abstract) Google Scholar
[11]	李天斌,肖学沛. 地下工程岩爆预测的综合集成方法[J]. 地球科学进展,2008,23(5):533 − 540. [LI Tianbin,XIAO Xuepei. Comprehensively integrated methods of rockburst prediction in underground engineering[J]. Advances in Earth Science,2008,23(5):533 − 540. (in Chinese with English abstract) doi: 10.3321/j.issn:1001-8166.2008.05.014 CrossRef Google Scholar
[12]	何怡帆,李天斌,曹海洋. 隧道施工期岩爆危险性评价的属性识别模型及工程应用[J]. 水文地质工程地质,2020,47(2):102 − 111. [HE Yifan,LI Tianbin,CAO Haiyang. Attribute recognition model of fatalness assessment of rockburst in tunnel construction and its application[J]. Hydrogeology & Engineering Geology,2020,47(2):102 − 111. (in Chinese with English abstract) Google Scholar
[13]	刘磊磊,张绍和,王晓密. 基于物元矩阵和理想点法的岩爆烈度预测[J]. 地下空间与工程学报,2016,12(1):205 − 212. [LIU Leilei,ZHANG Shaohe,WANG Xiaomi. Prediction of rockburst intensity based on matter-element matrix and ideal point method[J]. Chinese Journal of Underground Space and Engineering,2016,12(1):205 − 212. (in Chinese with English abstract) Google Scholar
[14]	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):2245 − 2252. [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):2245 − 2252. (in Chinese with English abstract) doi: 10.11779/CJGE201712013 CrossRef Google Scholar
[15]	董源,裴向军,张引,等. 基于组合赋权-云模型理论的岩爆预测研究[J]. 地下空间与工程学报,2018,14(增刊1):409 − 415. [DONG Yuan,PEI Xiangjun,ZHANG Yin,et al. Prediction of rock burst-based on combination weighting and cloud model theory[J]. Chinese Journal of Underground Space and Engineering,2018,14(Sup1):409 − 415. (in Chinese with English abstract) Google Scholar
[16]	王迎超,靖洪文,张强,等. 基于正态云模型的深埋地下工程岩爆烈度分级预测研究[J]. 岩土力学,2015,36(4):1189 − 1194. [WANG Yingchao,JING Hongwen,ZHANG Qiang,et al. A normal cloud model-based study of grading prediction of rockburst intensity in deep underground engineering[J]. Rock and Soil Mechanics,2015,36(4):1189 − 1194. (in Chinese with English abstract) Google Scholar
[17]	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. doi: 10.1007/s11069-017-3044-7 CrossRef Google Scholar
[18]	PU Y Y,APEL D B,XU H W. Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier[J]. Tunnelling and Underground Space Technology,2019,90:12 − 18. doi: 10.1016/j.tust.2019.04.019 CrossRef Google Scholar
[19]	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):2105 − 2113. doi: 10.1007/s11771-017-3619-1 CrossRef Google Scholar
[20]	付平,张新辉,刘元坤,等. 滇中香炉山引水隧洞工程区地应力场特征及断裂影响模糊综合评价[J]. 中国地质灾害与防治学报,2020,31(5):123 − 132. [FU Ping,ZHANG Xinhui,LIU Yuankun,et al. Characteristics of in-situ stress field and fuzzy comprehensive evaluation of the influence of active faults on the water diversion engineering of Xianglushan tunnel area in central Yunnan[J]. The Chinese Journal of Geological Hazard and Control,2020,31(5):123 − 132. (in Chinese with English abstract) Google Scholar
[21]	徐林生,王兰生. 岩爆形成机理研究[J]. 重庆大学学报(自然科学版),2001,24(2):115 − 117. [XU Linsheng,WANG Lansheng. Study on the mechanism of rockburst[J]. Journal of Chongqing University (Natural Science Edition),2001,24(2):115 − 117. (in Chinese with English abstract) Google Scholar
[22]	邢军,王建斌,蒋蕾,等. 圭嘎拉高速公路隧道地应力特征及岩爆预测[J]. 水文地质工程地质,2019,46(2):170 − 178. [XING Jun,WANG Jianbin,JIANG Lei,et al. In-situ stress characteristics and rock burst prediction of the Guigala Expressway tunnel[J]. Hydrogeology & Engineering Geology,2019,46(2):170 − 178. (in Chinese with English abstract) Google Scholar
[23]	彭华,崔巍,马秀敏,等. 南水北调西线第一期工程调水区水压致裂地应力测量及其工程意义[J]. 地质力学学报,2006,12(2):182 − 190. [PENG Hua,CUI Wei,MA Xiumin,et al. Hydrofracturing in-situ stress measurements of the water diversion area in the first stage of the South-North Water Diversion Project(western line)[J]. Journal of Geomechanics,2006,12(2):182 − 190. (in Chinese with English abstract) Google Scholar
[24]	徐俊帅,徐金明,涂齐亮. 基于地应力和岩体强度的岩爆预判[J]. 中国地质灾害与防治学报,2018,29(5):52 − 58. [XU Junshuai,XU Jinming,TU Qiliang. Prediction of rock burst based on field geostress and rock mass strength[J]. The Chinese Journal of Geological Hazard and Control,2018,29(5):52 − 58. (in Chinese with English abstract) doi: 10.16031/j.cnki.issn.1003-8035.2018.05.09 CrossRef Google Scholar
[25]	张鹏,孙治国,王秋宁,等. 木寨岭深埋隧道北段地应力测量与围岩稳定性分析[J]. 地质力学学报,2017,23(6):893 − 903. [ZHANG Peng,SUN Zhiguo,WANG Qiuning,et al. In-situ stress measurement and stability analysis of surrounding rocks in the north section of deep buried tunnel in Muzhailing[J]. Journal of Geomechanics,2017,23(6):893 − 903. (in Chinese with English abstract) Google Scholar
[26]	杨树新,姚瑞,崔效锋,等. 中国大陆与各活动地块、南北地震带实测应力特征分析[J]. 地球物理学报,2012,55(12):4207 − 4217. [YANG Shuxin,YAO Rui,CUI Xiaofeng,et al. Analysis of the characteristics of measured stress in Chinese mainland and its active blocks and North-South seismic belt[J]. Chinese Journal of Geophysics,2012,55(12):4207 − 4217. (in Chinese with English abstract) doi: 10.6038/j.issn.0001-5733.2012.12.032 CrossRef Google Scholar
[27]	李天斌, 孟陆波, 王兰生. 高地应力隧道稳定性及岩爆、大变形灾害防治[M]. 北京: 科学出版社, 2016 Google Scholar LI Tianbin, MENG Lubo, WANG Lansheng. Stability of high-stress tunnels and prevention of large-scale rockburst disasters [M]. Beijing: Science Press, 2016. （in Chinese） Google Scholar
[28]	JENSEN F V, NIELSEN T D. Bayesian networks and decision graphs[M]. New York: Springer, 2007. Google Scholar
[29]	PEARL J. A constraint propagation approach to probabilistic reasoning[J]. arXiv preprint: 1304.3422, 2013. Google Scholar
[30]	FENG X D,JIMENEZ R. Predicting tunnel squeezing with incomplete data using bayesian networks[J]. Engineering Geology,2015,195:214 − 224. doi: 10.1016/j.enggeo.2015.06.017 CrossRef Google Scholar
[31]	KORB K B, NICHOLSON A E. Bayesian artificial intelligence [M]. Boca Raton, Florida: CRC Press, 2010. Google Scholar