Prediction of rock burst in underground caverns based on rough set and extensible comprehensive evaluation

  • Yiguo Xue
  • Zhiqiang Li
  • Shucai Li
  • Daohong QiuEmail author
  • Yufan Tao
  • Lin Wang
  • Weimin Yang
  • Kai Zhang
Original Paper


In high terrestrial stress regions, rock burst is a major geological disaster influencing underground engineering construction significantly. How to carry out efficient and accurate rock burst prediction is still not resolved. In this paper, a new rock burst evaluation method based on rough set theory and extension theory is proposed. In the method the following seven indexes were selected as indices to evaluate and predict rock bursts: uniaxial compressive strength, ratio of rock strength to in situ stress, ratio of rock compressive strength to tensile strength, ratio of tangential stress and rock compressive strength, elastic strain energy index, depth of tunnel, and rock integrity. According to rough set theory, those indexes influencing rock bursts were investigated through attribute reduction operation to obtain four main influential indexes and the weight coefficients of each evaluation index were acquired by analysing the significance of conditional attribute. Thereafter, the main influential indexes and its weight were taken into the extension theory to predict the practical engineering. This method was applied to a practical case, underground caverns of Jiangbian hydropower station in China’s Sichuan province. It is proved that the evaluation results of the method were well consistent with real conditions.


Rock burst prediction Rough set theory Extension theory Underground caverns 



The work described in this paper was substantially supported by a grant from National Natural Science Foundation of China (CN) (51309144, 51379112, 51422904) and the National Program on Key Basic Research Project of China (973 Program) (No. 2013CB036002).


  1. Cai W (1997) Extension engineering methods. Science Press, Beijing, pp 16–31Google Scholar
  2. Chen DG, Wang CZ, Hu QH (2007) A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets. Inform Sci 177(17):3500–3518. doi: 10.1016/j.ins.2007.02.041 Google Scholar
  3. Chen X, Sun JZ, Zhang JK et al (2009) Judgment indexes and classification criteria of rock-burst with the extension judgment method. Chin Civil Eng J 42(09):82–88 (In Chinese) Google Scholar
  4. Chen WZ, Lv SP, Guo XH et al (2010) Unloading confining pressure for brittle rock and mechanism of rock burst. Chin J Geotech Eng 32(6):963–969 (In Chinese) Google Scholar
  5. Cook NGW, Hoek E, Pretorius JPG et al (1966) Rock mechanics applied to the study of rockbursts. J S Afr I Min Metall 66(12):436–528Google Scholar
  6. Dong LJ, Li XB, Peng K (2013) Prediction of rock burst classification using random forest. Trans Nonferr Metals Soc China 23(2):472–477Google Scholar
  7. Feng XT, Zhao HB (2002) Prediction of rockburst using support vector machine. J Northeast Univ (Nat Sci) 23(1):57–59 (In Chinese) Google Scholar
  8. Gong FQ, Li XB (2007) A distance discriminate analysis method for prediction of possibility and classification of rockburst and its application. Chin J Rock Mech Eng 26(5):1013–1017 (In Chinese) Google Scholar
  9. Greco S, Matarazzo B, Slowinski R (2001) Rough sets theory for multicriteria decision analysis. Eur J Oper Res 129(1):1–47. doi: 10.1016/S0377-2217(00).00167-3 Google Scholar
  10. Gu MC, He FL, Chen CZ (2002) Study of rockburst in Qingling tunnel. Chin J Rock Mech Eng 21(9):1324–1329 (In Chinese) Google Scholar
  11. Hao J, Shi KB, Wang XL et al (2016) Application of cloud model to rating of rockburst based on rough set of FCM algorithm. Rock Soil Mech 37(03):859–866 (In Chinese) Google Scholar
  12. He MC, Miao JL, Feng JL (2010) Rock burst process of limestone and its acoustic emission characteristics under true-triaxial unloading conditions. Int J Rock Mech Min 47(2):286–298. doi: 10.1016/j.ijrmms.2009.09.003 Google Scholar
  13. Jia RS, Liu C, Sun HM et al (2015) A situation assessment method for rock burst based on multi-agent information fusion. Comput Electr Eng 45:22–32Google Scholar
  14. Jiang T, Huang ZQ, Zhao YY (2004) Dynamically weighted grey optimization model for rockburst risk forecasting and its application to western route of South–North Water Transfer Project. Chin J Rock Mech Eng 23(7):1104–1108 (In Chinese) Google Scholar
  15. Jiang Q, Feng XT, Xiang TBV (2010) Rockburst characteristics and numerical simulation based on a new energy index: a case study of a tunnel at 2,500 m depth. B Eng Geol Environ 69:381–388. doi: 10.1007/s10064-010-0275-1 Google Scholar
  16. Jun Y (2009) Application of extension theory in misfire fault diagnosis of gasoline engines. Exp Syst Appl 36:1217–1221. doi: 10.1016/j.eswa.2007.11.012 Google Scholar
  17. Lee SM, Park BS, Lee SW (2004) Analysis of rock bursts that have occurred in a waterway tunnel in Korea. Int J Rock Mech Min 41:911–916. doi: 10.1016/j.ijrmms.2004.03.157 Google Scholar
  18. Li X, Li Z, Wang E et al (2016) Extraction of microseismic waveforms characteristics prior to rock burst using Hilbert–Huang transform. Measurement 91:101–113Google Scholar
  19. Li N, Feng X, Jimenez R (2017) Predicting rock burst hazard with incomplete data using Bayesian networks. Tunn Undergr Space Technol 61:61–70Google Scholar
  20. Ortlepp WD, Stacey TR (1994) Rockburst mechanisms in tunnels and shafts. Tunn Undergr Sp Tech 9(1):59–65. doi: 10.1016/0886-7798(94)90010-8 Google Scholar
  21. Pawlak Z (1982) Rough sets. Int J Comput Inform Sci 11(5):341–356. doi: 10.1007/BF01001956 Google Scholar
  22. Shang YJ, Zhang JJ, Fu BJ (2013) Analyses of three parameters for strain mode rockburst and expression of rockburst potential. Chin J Rock Mech Eng 32(08):1520–1527 (In Chinese) Google Scholar
  23. Su GS, Feng XT (2005) Parameter identification of constitutive model for hard rock under high in-situ stress condition using particle swarm optimization algorithm. Chin J Rock Mech Eng 24(17):3029–3034 (In Chinese) Google Scholar
  24. Wang YH, Li WD, Li QG et al (1998) Fuzzy comprehensive evaluation method of rock burst prediction. Chin J Rock Mech Eng 17(5):493–501 (In Chinese) Google Scholar
  25. Wang MH, Tseng YF, Chen HC, Chao KH (2009) A novel clustering algorithm based on the extension theory and genetic algorithm. Expert Syst Appl 36:8269–8276. doi: 10.1016/j.eswa.2008.10.010 Google Scholar
  26. Wang YC, Shang YQ, Sun YH et al (2010) Study of prediction of rockburst intensity based on efficacy coefficient method. Rock Soil Mech 31(2):529–534 (In Chinese) Google Scholar
  27. Wang YC, Jing HW, Ji XW et al (2014) Model for classification and prediction of rock burst intensity in a deep underground engineering with rough set and efficacy coefficient method. J Cent South Univ (Sci Technol) 6:1992–1997 (In Chinese) Google Scholar
  28. Xu F, Xu WY (2010) Projection pursuit model based on particle swarm optimization for rock burst prediction. Chin J Geotech Eng 3(5):718–723 (In Chinese) Google Scholar
  29. Ye J (2009) Application of extension theory in misfire fault diagnosis of gasoline engines. Expert Syst Appl 36(2):1217–1221. doi: 10.1016/j.eswa.2007.11.012 Google Scholar
  30. Zhang P, Chen JP, Qiu DH (2009) Evaluation of tunnel surrounding rock quality with extenics based on rough set. Rock Soil Mech 30(1):246–250 (In Chinese) Google Scholar
  31. Zhang LM, Wang ZQ, Shi L et al (2012a) Acoustic emission characteristics of marble during failure process under different stress paths. Chin J Rock Mechan Eng 31(6):1230–1236 (In Chinese) Google Scholar
  32. Zhang LW, Zhang DY, Qiu DH (2012b) Application of radial basis function neural network to geostress field back analysis. Rock Soil Mech 33(3):799–804 (In Chinese) Google Scholar
  33. Zhang S, Meng FS, Zhang J et al (2013) Research on sedimentary microfacies extension identification method research based on the rough set theory. Period Ocean Univ China 6:76–80 (In Chinese) Google Scholar
  34. Zhang Y, Xiao PX, Chen LJ (2014) Method of layout design based on ratio of rock strength to in-situ stress for large underground caverns. Chin J Rock Mech Eng 33(11):2314–2331 (In Chinese) Google Scholar
  35. Zhou K, Lin Y, Deng H et al (2016) Prediction of rock burst classification using cloud model with entropy weight. Trans Nonferr Metals Soc China 26(7):1995–2002Google Scholar
  36. Zhu JJ, Zheng JG, Li JB (2012) Rough classification algorithm for uncertain extension group decision-making. Control Decis 27(6):850–854 (In Chinese) Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Yiguo Xue
    • 1
  • Zhiqiang Li
    • 1
  • Shucai Li
    • 1
  • Daohong Qiu
    • 1
    Email author
  • Yufan Tao
    • 1
  • Lin Wang
    • 1
  • Weimin Yang
    • 1
  • Kai Zhang
    • 1
  1. 1.Research Center of Geotechnical and Structural EngineeringShandong UniversityJinanChina

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