Advertisement

Evaluation of rock burst intensity based on annular grey target decision-making model with variable weight

  • Xinlong Zhou
  • Guang ZhangEmail author
  • Yinghua Song
  • Shaohua Hu
  • Mingze Liu
  • Junzhe Li
Original Paper
  • 58 Downloads

Abstract

Rock burst is a dynamic and complex phenomenon caused by numerous factors in underground excavating. It is very difficult to make evaluations accurately, especially under incomplete information. In this paper, a methodology for rock burst intensity evaluation is proposed based on grey target decision-making theory and variable weight synthesis thought. Some main factors that influence rock burst intensity are systematically analyzed to establish the evaluation index system. A balance function is introduced to investigate the variability of attribute weight, and then the weights of contribution factors are determined by utilizing variable weight synthesis, in conjunction with grey entropy algorithm. With respect to incomplete information in reality, the annular grey target theory is first proposed to address risk level of rock burst. Different distribution sets of bull’s eye distance are constructed to quantitatively represent corresponding intensity degree. Eventually, the application and performance comparison are carried out to demonstrate the feasibility and precision of the proposed model. It is demonstrated that the outcomes of the proposed model completely coincide with actual states. Compared with rough set theory and Russenes criterion, the proposed model can efficiently reduce decision ambiguity and produce a distinct risk measurement. It provides a new resolution for the research of rock burst evaluation under limited data.

Keywords

Rock burst Annular grey target decision-making method Variable weight Bull’s eye distance 

Notes

Acknowledgments

The authors are grateful to the anonymous referees for their helpful comments.

Funding information

This research is supported by the Fundamental Research Funds for the Central Universities under Grant No. 185208001, the National Natural Science Foundation of China under Grant No. 51609184, the National Key Research and Development Program of China under Grant No. 2016YFC0802509, and the National Key Research and Development Program of China under Grant No. 2017YFC0804600.

References

  1. Adoko AC, Gokceoglu C, Wu L, Zuo QJ (2013) Knowledge-based and data-driven fuzzy modeling for rockburst prediction. Int J Rock Mech Min Sci 61:86–95.  https://doi.org/10.1016/j.ijrmms.2013.02.010 CrossRefGoogle Scholar
  2. Afraei S, Shahriar K, Madani SH (2018) Statistical assessment of rock burst potential and contributions of considered predictor variables in the task. Tunn Undergr Space Technol 72:250–271.  https://doi.org/10.1016/j.tust.2017.10.009 CrossRefGoogle Scholar
  3. Afraei S, Shahriar K, Madani SH (2019) Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, section 1: literature review and data preprocessing procedure. Tunn Undergr Space Technol 83:324–353.  https://doi.org/10.1016/j.tust.2018.09.022 CrossRefGoogle Scholar
  4. Akdag S, Karakus M, Taheri A, Nguyen G, Manchao H (2018) Effects of thermal damage on strain burst mechanism for brittle rocks under true-triaxial loading conditions. Rock Mech Rock Eng 51:1657–1682.  https://doi.org/10.1007/s00603-018-1415-3 CrossRefGoogle Scholar
  5. Aubertin M, Gill DE, Simon R (1994) On the use of the brittleness index modified (BIM) to estimate the post-peak behavior of rocks. Aqua Fenn 23:24–25Google Scholar
  6. Cai S, Zhang L, Zhou W (2005) Research on prediction of rock burst in deep hard-rock mass. J Saf Sci Technol 1:17–20Google Scholar
  7. Castro LAM, Bewick RP, Carter TG (2012) An overview of numerical modelling applied to deep mining. In: Innovative numerical modelling in geomechanics. CRC Press/Taylor & Francis, London, pp 393–414CrossRefGoogle Scholar
  8. Chen S, Li Z, Xu Q (2006) Grey target theory based equipment condition monitoring and wear mode recognition. Wear 260:438–449.  https://doi.org/10.1016/j.wear.2005.02.085 CrossRefGoogle Scholar
  9. Deng J (2010) Grey entropy and grey target decision making. J Grey Syst 1:1–4Google Scholar
  10. Ding X, Wu J, Li J, Chengjun L (2003) Artificial neural network for forecasting and classification of rockbursts. J Hohai Univ Sci 31:424–427Google Scholar
  11. Dong L, Li X, Peng K (2013) Prediction of rockburst classification using random forest. Trans Nonferrous Met Soc China 23:472–477.  https://doi.org/10.1016/S1003-6326(13)62487-5 CrossRefGoogle Scholar
  12. Du Z, Xu M, Liu Z, Wu X (2006) Laboratory integrated evaluation method for engineering wall rock rock-burst. Gold 27:26–29Google Scholar
  13. Fan P (2000) Prediction of the rock burst in the deep-well development of Dongguashan Deposit and its prevention control measures. Met Mine 289:41–43Google Scholar
  14. Faradonbeh RS, Taheri A (2018) Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques. Vector-Borne Zoonotic Dis 12:645–649.  https://doi.org/10.1007/s00366-018-0624-4 CrossRefGoogle Scholar
  15. Gao W (2015) Forecasting of rockbursts in deep underground engineering based on abstraction ant colony clustering algorithm. Nat Hazards 76:1625–1649.  https://doi.org/10.1007/s11069-014-1561-1 CrossRefGoogle Scholar
  16. Guo W, Li Y, Yin D, Zhang S, Sun X (2016) Mechanisms of rock burst in hard and thick upper strata and rock-burst controlling technology. Arab J Geosci 9.  https://doi.org/10.1007/s12517-016-2596-2
  17. Hao J, Liu D, Li Z, Chen Z, Kong L (2012) Power system load forecasting based on fuzzy clustering and gray target theory. Energy Procedia 16:1852–1859.  https://doi.org/10.1016/j.egypro.2012.01.284 CrossRefGoogle Scholar
  18. Hao J, Shi K, Wang X et al (2016) Application of cloud model to rating of rockburst based on rough set of FCM algorithm. Rock Soil Mech 37:859–866.  https://doi.org/10.16285/j.rsm.2016.03.031 CrossRefGoogle Scholar
  19. Hoek E, Brown ET (1997) Practical estimates of rock mass strength. Int J Rock Mech Min Sci 34:1165–1186CrossRefGoogle Scholar
  20. Hosseini N (2017) Evaluation of the rockburst potential in longwall coal mining using passive seismic velocity tomography and image subtraction technique. J Seismol 21:1101–1110.  https://doi.org/10.1007/s10950-017-9654-4 CrossRefGoogle Scholar
  21. Jia Y, Fan Z (1990) Rock burst mechanism and criterion in hydraulic underground cavern. Water Power:30–34Google Scholar
  22. Kidybiiqski A (1981) Bursting liability indices of coal. Int J Rock Mech Min Sci Geomech Abstr 18:295–304CrossRefGoogle Scholar
  23. Li S, Feng X, Wang Y, Yang N (2001) Evaluation of rockburst in a deep hard rock mine. J Northeast Univ (Natural Sci) 22:60–63Google Scholar
  24. Li N, Feng X, Jimenez R (2017) Predicting rock burst hazard with incomplete data using Bayesian networks. Tunn Undergr Space Technol Inc Trenchless Technol Res 61:61–70.  https://doi.org/10.1016/j.tust.2016.09.010 CrossRefGoogle Scholar
  25. Liu L, Chen Z, Wang L (2015) Rock burst laws in deep mines based on combined model of membership function and dominance-based rough set. J Cent South Univ 22:3591–3597.  https://doi.org/10.1007/s11771-015-2899-6 CrossRefGoogle Scholar
  26. Luo D, Wang X (2012) The multi-attribute grey target decision method for attribute value within three-parameter interval grey number. Appl Math Model 36:1957–1963.  https://doi.org/10.1016/j.apm.2011.07.074 CrossRefGoogle Scholar
  27. Lv Q, Sun H, Shang Y et al (2005) Comprehensive study on prediction of rockburst in deep and over-length highway tunnel. Chin J Rock Mech Eng 24:2982–2988Google Scholar
  28. Maleki H, Lawson H (2017) Analysis of geomechanical factors affecting rock bursts in sedimentary rock formations. Procedia Eng 191:82–88.  https://doi.org/10.1016/j.proeng.2017.05.157 CrossRefGoogle Scholar
  29. Manouchehrian A, Cai M (2018) Numerical modeling of rockburst near fault zones in deep tunnels. Tunn Undergr Space Technol 80:164–180.  https://doi.org/10.1016/j.tust.2018.06.015 CrossRefGoogle Scholar
  30. Mitri HS (2007) Assessment of horizontal pillar burst in deep hard rock mines. Int J Risk Assess Manag 7:695–707CrossRefGoogle Scholar
  31. Richard S (1999) Analysis of fault-slip mechanisms in hard rock mining. McGill University (Canada)Google Scholar
  32. Russenes B. (1974) Analysis of rock spalling for tunnels in steep valley sides. Nor Inst TechnolGoogle Scholar
  33. Russo G (2014) An update of the “multiple graph” approach for the preliminary assessment of the excavation behaviour in rock tunnelling. Tunn Undergr Space Technol 41:74–81.  https://doi.org/10.1016/j.tust.2013.11.006 CrossRefGoogle Scholar
  34. Rydert JA (1988) Excess shear stress in the assessment of geologically hazardous situations. J South African Inst Min Metall 88:27–39Google Scholar
  35. Shu L, Wang L, Peng J (1998) A study of fuzzy synthetic judgement method of burst prediction for Qinling Railway Tunnel. J Sichuan Union Univ (Eng Sci Ed) 2:55–60Google Scholar
  36. Singh SP (1989) Classification of mine workings according to their rockburst proneness. Min Sci Technol 8:253–262CrossRefGoogle Scholar
  37. Sousa RL, Einstein HH (2012) Risk analysis during tunnel construction using Bayesian networks: Porto Metro case study. Tunn Undergr Space Technol 27:86–100.  https://doi.org/10.1016/j.tust.2011.07.003 CrossRefGoogle Scholar
  38. Tang L, Wang W (2002) New rock burst proneness index. Chin J Rock Mech Eng 21:874–878Google Scholar
  39. Turchaninov IA, Markov GA, Gzovsky MV, Kazikayev DM, Frenze UK, Batugin SA, Chabdarova UI (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 1. Phys Earth Planet Inter 6:229–234CrossRefGoogle Scholar
  40. Wang JA, Park HD (2001) Comprehensive prediction of rockburst based on analysis of strain energy in rocks. Tunn Undergr Space Technol 16:49–57.  https://doi.org/10.1016/S0886-7798(01)00030-X CrossRefGoogle Scholar
  41. Wang Y, Li W, Li Q et al (1998) Method of fuzzy comprehensive evaluations for rockburst prediction. Chin J Rock Mech Eng 17:493–501Google Scholar
  42. Wang S, Li C, Yan W, Zou Z, Chen W (2017) Multiple indicators prediction method of rock burst based on microseismic monitoring technology. Arab J Geosci 10:1–8.  https://doi.org/10.1007/s12517-017-2946-8 CrossRefGoogle Scholar
  43. Wu D, Yang J (2005) Prediction and countermeasure for rockburst in Cangling Mountain Highway Tunnel. Chin J Rock Mech Eng 24:3965–3971Google Scholar
  44. Wu Y, Zhang W (1997) Evaluation of the bursting proneness of coal by means of its failure duration. In: 4. international symposium on rockbursts and seismicity in mines. Netherlands, Netherlands, pp 285–288Google Scholar
  45. Yan S, Liu S, Liu J, Wu L (2015) Dynamic grey target decision making method with grey numbers based on existing state and future development trend of alternatives. J Intell Fuzzy Syst 28:2159–2168.  https://doi.org/10.3233/IFS-141497 CrossRefGoogle Scholar
  46. Yang T, Li G (2007) Study on rockburst prediction method based on the prior knowledge. Chin J Rock Mech Eng 19:429–431Google Scholar
  47. Zeng W, Li D, Wang P (2016) Variable weight decision making and balance function analysis based on factor space. Int J Inf Technol Decis Mak 15:999–1014.  https://doi.org/10.1142/S021962201650022X CrossRefGoogle Scholar
  48. Zhang X (2005) Rock burst prediction model basedon artificial neural network. Yangtze River 36:17–18Google Scholar
  49. Zhang Y (2012) Application of extensible comprehensive evaluation for rockburst prediction in a hydro-tunnel. J Shandong Univ (Eng Sci) 42:58–63Google Scholar
  50. Zhang C, Feng X, Zhou H, Wenping W (2012a) Case histories of four extremely intense rockbursts in deep tunnels. Rock Mech Rock Eng 45:275–288.  https://doi.org/10.1007/s00603-011-0218-6 CrossRefGoogle Scholar
  51. Zhang JJ, Fu BJ, Li ZK, et al (2012b) Criterion and classification for strain mode rockbursts based on five-factor comprehensive method. In: International Society for Rock Mechanics and Rock Engineering. pp 1435–1440Google Scholar
  52. Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50:629–644.  https://doi.org/10.1016/j.ssci.2011.08.065 CrossRefGoogle Scholar
  53. Zhu J, Hipel KW (2012) Multiple stages grey target decision making method with incomplete weight based on multi-granularity linguistic label. Inf Sci (Ny) 212:15–32.  https://doi.org/10.1016/j.ins.2012.05.011 CrossRefGoogle Scholar
  54. Zhu F, Pan C, Cao P (2002) Fuzzy synthetical evaluation in rock bursting tendency in Dongguazhan Deposit based on gray relation degree analysis. Nonferrous Met 54:71–74Google Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Xinlong Zhou
    • 1
  • Guang Zhang
    • 1
    Email author
  • Yinghua Song
    • 2
    • 3
  • Shaohua Hu
    • 1
  • Mingze Liu
    • 1
  • Junzhe Li
    • 1
  1. 1.School of Resources and Environmental EngineeringWuhan University of TechnologyWuhanChina
  2. 2.China Research Center for Emergency ManagementWuhan University of TechnologyWuhanChina
  3. 3.Hubei Collaborative Innovation Center for Early Warning and Emergency Response TechnologyWuhanChina

Personalised recommendations