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Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach

  • Ebrahim GhasemiEmail author
  • Hasan Gholizadeh
  • Amoussou Coffi Adoko
Original Article
  • 43 Downloads

Abstract

Based on reported statistics, rockburst phenomenon is the main cause of many casualties and accidents occurred during the construction of deep underground structures. Therefore, its prediction in initial stages of design has a remarkable role on enhancement of safety. In this paper, two models have been developed for rockburst evaluation using the C5.0 decision tree classifier. The first model has been applied for prediction of rockburst occurrence and the second model for prediction of rockburst intensity. These models have been developed based on a database including 174 rockburst case histories. In both models, stress coefficient, rock brittleness coefficient, and the elastic strain energy index are the predictive variables. These models are easy to use and do not require extensive knowledge. Based on decision rules derived from these models, the rockburst occurrence and intensity can be evaluated easily. The results revealed that the proposed approach is a useful and robust technique for long-term prediction of rockburst.

Keywords

Underground structures Rockburst Long-term prediction Decision tree C5.0 classifier 

Notes

Compliance with ethical standards

Conflict of interest

It is declared that all the authors totally agree that there is no conflict of interest on this paper.

Supplementary material

366_2018_695_MOESM1_ESM.docx (39 kb)
Supplementary material 1 (DOCX 38 KB)

References

  1. 1.
    Ortlepp WD (1997) Rock fracture and rockbursts—an illustrative study. The South African Institute of Mining and Metallurgy, Johannesburg, 98Google Scholar
  2. 2.
    Ortlepp WD, Stacey TR (1998) Performance of tunnel support under large deformation static and dynamic loading. Tunn Undergr Sp Tech 13:15–21Google Scholar
  3. 3.
    Brauner G (1994) Rockbursts in coal mines and their prevention. AA Balkema, AvereestGoogle Scholar
  4. 4.
    Dou L, Chen T, Gong S, He H, Zhang S (2012) Rockburst hazard determination by using computed tomography technology in deep workface. Saf Sci 50:736–740Google Scholar
  5. 5.
    Cai M (2013) Principles of rock support in burst-prone ground. Tunn Undergr Sp Tech 36:46–56Google Scholar
  6. 6.
    Li T, Ma C, Zhu M, Meng L, Chen G (2017) Geomechanical types and mechanical analyses of rockbursts. Eng Geol 222:72–83Google Scholar
  7. 7.
    He M, Ren F, Liu D (2018) Rockburst mechanism research and its control. Int J Min Sci Tech 28:829–837Google Scholar
  8. 8.
    Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Sp Tech 81:632–659Google Scholar
  9. 9.
    Blake W, Hedley DGF (2003) Rockbursts: case studies from North American Hard-Rock Mines. Society for Mining, Metallurgy, and Exploration (SME), Littleton, Colorado, USGoogle Scholar
  10. 10.
    Feng XT (2018) Rockburst: mechanisms, monitoring, warning and mitigation. Butterworth-Heinemann, OxfordGoogle Scholar
  11. 11.
    Keneti A, Sainsbury BA (2018) Review of published rockburst events and their contributing factors. Eng Geol 246:361–373Google Scholar
  12. 12.
    Cai M (2016) Prediction and prevention of rockburst in metal mines—a case study of Sanshandao gold mine. J Rock Mech Geotech Eng 8:204–211Google Scholar
  13. 13.
    Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30:04016003Google Scholar
  14. 14.
    Afraei S, Shahriar K, Madani SH (2018) Statistical assessment of rock burst potential and contributions of considered predictor variables in the task. Tunn Undergr Sp Tech 72:250–271Google Scholar
  15. 15.
    Feng X, Wang L (1994) Rockburst prediction based on neural networks. Trans Nonferrous Meterol Soc China 4:7–14Google Scholar
  16. 16.
    Chen HJ, Li NH, Ni DX, Shang YQ (2003) Prediction of rockburst by artificial neural network. Chin J Rock Mech Eng 22:762–768Google Scholar
  17. 17.
    Guo L, Li XB, Yan XM, Xiong LH (2005) Rock burst prediction methods based on BP network theory. Ind Saf Environ Prot 31:32–35Google Scholar
  18. 18.
    Zhang GC, Gao Q, Du JQ, Li KK (2013) Rockburst criterion based on artificial neural networks and nonlinear regression. J Central South Univ (Sci Techno) 44:2977–2981Google Scholar
  19. 19.
    He M, e Sousa LR, Miranda T, Zhu G (2015) Rockburst laboratory tests database—application of data mining techniques. Eng Geol 185:116–130Google Scholar
  20. 20.
    Bai YF, Deng J, Dong LJ, Li X (2009) Fisher discriminant analysis model of rock burst prediction and its application in deep hard rock engineering. J Central South Univ (Sci Techno) 40:1417–1422Google Scholar
  21. 21.
    Zhou J, Shi XZ, Dong L, Hu HY, Wang HY (2010) Fisher discriminant analysis model and its application for prediction of classification of rockburst in deep buried long tunnel. J Coal Sci Eng (China) 16(2):144–149Google Scholar
  22. 22.
    Yu HC, Liu HN, Lu XS, Liu HD (2009) Prediction method of rock burst proneness based on rough set and genetic algorithm. J Coal Sci Eng (China) 15(4):367–373MathSciNetGoogle Scholar
  23. 23.
    Yang JL, Li XB, Zhou ZL, Lin Y (2010) A Fuzzy assessment method of rock-burst prediction based on rough set theory. Metal Mine 6:26–29Google Scholar
  24. 24.
    Li TZ, Li YX, Yang XL (2017) Rock burst prediction based on genetic algorithms and extreme learning machine. J Central South Univ 24:2105–2113Google Scholar
  25. 25.
    Zhao HB (2005) Classification of rockburst using support vector machine. Rock Soil Mech 26(4):642–644Google Scholar
  26. 26.
    Zhu YH, Liu XR, Zhou JP (2008) Rockburst prediction analysis based on v-SVR algorithm. J Chin Coal Soc 33:277–281Google Scholar
  27. 27.
    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 50:629–644Google Scholar
  28. 28.
    Dong LJ, Li XB, Peng K (2013) Prediction of rockburst classification using random forest. Trans Nonferrous Meterol Soc China 23(2):472–477Google Scholar
  29. 29.
    Li B, Liu Y (2015) Determination of classification of rock burst risk based on random forest approach and its application. Sci Technol Rev 33:57–62Google Scholar
  30. 30.
    Wang Y, Li W, Li Q, Tan G (1998) Method of fuzzy comprehensive evaluations for rockburst prediction. Chinese J Rock Mech Eng 17:493–501Google Scholar
  31. 31.
    Liu ZJ, Yuan QP, Li JL (2008) Application of fuzzy probability model to prediction of classification of rockburst intensity. Chin J Rock Mech Eng 27:3095–3103Google Scholar
  32. 32.
    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 61:86–95Google Scholar
  33. 33.
    Wang C, Wu A, Lu H, Bao T, Liu X (2015) Predicting rockburst tendency based on fuzzy matter-element model. Int J Rock Mech Min Sci 75:224–232Google Scholar
  34. 34.
    Liu Z, Shao J, Xu W, Meng Y (2013) Prediction of rock burst classification using the technique of cloud models with attribution weight. Nat Hazards 68:549–568Google Scholar
  35. 35.
    Zhou KP, Lin Y, Deng HW, Li JL, Liu CJ (2016) Prediction of rock burst classification using cloud model with entropy weight. Trans Nonferrous Meterol Soc China 26:1995–2002Google Scholar
  36. 36.
    Zhou J, Shi XZ, Huang RD, Qiu XY, Chen C (2016) Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines. Trans Nonferrous Meterol Soc China 26:1938–1945Google Scholar
  37. 37.
    Gao W (2010) Prediction of rock burst based on ant colony clustering algorithm. Chin J Geotech Eng 32:874–880Google Scholar
  38. 38.
    Gao W (2015) Forecasting of rockbursts in deep underground engineering based on abstraction ant colony clustering algorithm. Nat Hazards 76:1625–1649Google Scholar
  39. 39.
    Li N, Feng X, Jimenez R (2017) Predicting rock burst hazard with incomplete data using Bayesian networks. Tunn Undergr Sp Tech 61:61–70Google Scholar
  40. 40.
    Sousa LR, Miranda T, Sousa RL, Tinoco J (2017) The use of data mining techniques in rockburst risk assessment. Engineering 3:552–558Google Scholar
  41. 41.
    Faradonbeh RS, Taheri A (2018) Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques. Comput Eng.  https://doi.org/10.1007/s00366-018-0624-4 Google Scholar
  42. 42.
    Lin Y, Zhou K, Li J (2018) Application of cloud model in rock burst prediction and performance comparison with three machine learning algorithms. IEEE Access 6:30958–30968Google Scholar
  43. 43.
    Li N, Jimenez R (2018) A logistic regression classifier for long-term probabilistic prediction of rock burst hazard. Nat Hazards 90:197–215Google Scholar
  44. 44.
    Pu Y, Apel DB, Lingga B (2018) Rockbusrt prediction in kimberlite using decision tree with incomplete data. J Sustain Min 17:158–165Google Scholar
  45. 45.
    Hwang S, Guevarra IF, Yu B (2009) Slope failure prediction using a decision tree: A case of engineered slopes in South Korea. Eng Geol 104:126–134Google Scholar
  46. 46.
    Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering 901095Google Scholar
  47. 47.
    Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116:274–283Google Scholar
  48. 48.
    Lee S, Park I (2013) Application od decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines. J Environ Manag 127:166–176Google Scholar
  49. 49.
    Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365Google Scholar
  50. 50.
    Bui DT, Ho TC, Revhaug I, Pradhan B, Nguyen DB (2014) Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based, J48 decision tree classifier and its ensembles. In: Buchroithner M, Prechtel N, Burghardt D (eds) Cartography from pole to pole. Springer-Verlag, Berlin, pp 303–317Google Scholar
  51. 51.
    Dindarloo SR, Siami-Irdemoosa E (2015) Maximum surface settlement based classification of shallow tunnels in soft ground. Tunn Undergr Sp Tech 49:320–327Google Scholar
  52. 52.
    Ghasemi E, Gholizadeh H (2018) Prediction of squeezing potential in tunneling projects using data mining-based techniques. Geotech Geol Eng.  https://doi.org/10.1007/s10706-018-0705-6 Google Scholar
  53. 53.
    Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11Google Scholar
  54. 54.
    Zaremotlagh S, Hezarkhani A (2017) The use of decision tree induction and artificial neural networks for recognizing the geochemical distribution patterns of LREE in the Choghart deposit, Central Iran. J Afr Earth Sci 128:37–46Google Scholar
  55. 55.
    Ghasemi E, Kalhori H, Bagherpour R (2017) Stability assessment of hard rock pillars using two intelligent classification techniques: a comparative study. Tunn Undergr Sp Tech 68:32–37Google Scholar
  56. 56.
    Xu C, Liu X, Wang E, Zheng Y, Wang S (2018) Rockburst prediction and classification based on the ideal-point method of information theory. Tunn Undergr Sp Tech 81:382–390Google Scholar
  57. 57.
    Ortlepp WD, Stacey TR (1994) Rockburst mechanisms in tunnels and shafts. Tunn Undergr Space Technol 9:59–65Google Scholar
  58. 58.
    Tang CA, Hudson A (2010) Rock failure mechanisms: explained and illustrated. CRC Press, Boca RatonGoogle Scholar
  59. 59.
    Kaiser PK, Tannant DD, McCreath DR (1996) Canadian rockburst support handbook. Geomechanics Research Centre, Laurentian University, Sudbury, OntarioGoogle Scholar
  60. 60.
    Farid DM, Zhang L, Rahman CM, Hossain M, Strachan R (2014) Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Syst Appl 41:1937–1946Google Scholar
  61. 61.
    Zaremotlagh S, Hezarkhani A (2016) A geochemical modeling to predict the different concentrations of REE and their hidden patterns using several supervised learning methods: Choghart iron deposit, bafq, Iran. J Geochem Explor 165:35–48Google Scholar
  62. 62.
    Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, BelmontzbMATHGoogle Scholar
  63. 63.
    Michael JA, Gordon SL (1997) Data mining technique: for marketing, sales and customer support. Wiley, New YorkGoogle Scholar
  64. 64.
    Quinlan JR (1986) Introduction of decision trees. Mach Learn 1:81–106Google Scholar
  65. 65.
    Loh WY, Shih YS (1997) Split selection methods for classification trees. Stat Sinica 7:815–840MathSciNetzbMATHGoogle Scholar
  66. 66.
    Quinlan JR (1993) C4.5: programs for machine learning, first ed. Morgan Kaufmann, San MateoGoogle Scholar
  67. 67.
    Quinlan JR (2003) C4.5: programs for machine learning, second edn. Morgan Kaufmann, San MateoGoogle Scholar
  68. 68.
    Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Los AltoszbMATHGoogle Scholar
  69. 69.
    Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques, third edn. Morgan Kaufmann, WalthamzbMATHGoogle Scholar
  70. 70.
    Rokach L, Maimon O (2015) Data mining with decision trees: theory and application, second edn. World Scientific, SingaporezbMATHGoogle Scholar
  71. 71.
    Medvedev V, Kurasova O, Bernataviciene J, Treigys P, Marcinkevicius V, Dzemyda G (2017) A new web-based solution for modelling data mining processes. Simul Model Pract Th 76:34–46zbMATHGoogle Scholar
  72. 72.
    Russenes B (1974) Analysis of Rock Spalling for Tunnels in Steep Valley Sides. Norwegian Institute of TechnologyGoogle Scholar
  73. 73.
    Kidybinski A (1981) Bursting liability indices of coal. Int J Rock Mech Min Sci Geomech Abstr 18:295–304Google Scholar
  74. 74.
    Singh SP (1989) Classification of mine workings according to their rockburst proneness. Min Sci Technol 8:253–262Google Scholar
  75. 75.
    Zhang JJ, Fu BJ, Li ZK, Song SW, Shang YJ (2012) Criterion and classification for strain mode rockbursts based on five factor comprehensive method. In: Proc., 12th ISRM Int. Congress on Rock Mechanics, Harmonising Rock Engineering and the Environment, Q. Qian and J. Zhou, eds., Taylor & Francis Group, London, 1435–1440Google Scholar
  76. 76.
    Jia YR, Fan ZQ (1991) Hydraulic underground cavern medium of rockburst mechanism and criterion. Water Power 6:30–34Google Scholar
  77. 77.
    Tang SH, Wu ZJ, Chen XH (2003) Approach to occurrence and mechanism of rockburst in deep underground mines. Chin J Rock Mech Eng 22:1250–1254Google Scholar
  78. 78.
    Xiao XP (2005) A study on the prediction and prevention of rockburst traffic tunnel of Jinping II hydropower station. Master’s thesis, Chengdu Univ. of Technology, Chengdu, ChinaGoogle Scholar
  79. 79.
    Xia BW (2006) Study on prediction and forecast of geologic disaster in highway tunnel construction. Master’s thesis, Chongqing Univ., Chongqing, ChinaGoogle Scholar
  80. 80.
    Zhao XF (2007) Study on the high geo-stress and rockburst of the deep-lying long tunnel. Master’s thesis, North China Univ. of Water Resources and Electric Power, Zhengzhou, ChinaGoogle Scholar
  81. 81.
    Wang JL, Chen JP, Yang J, Que JS (2009) Method of distance discriminant analysis for determination of classification of rockburst. Rock Soil Mech 30:2203–2208Google Scholar
  82. 82.
    Xu MG, Du ZJ, Yao GH, Liu ZP (2008) Rockburst prediction of Chengchao iron mine during deep mining. Chin J Rock Mech Eng 27:2921–2928Google Scholar
  83. 83.
    Guo C, Zhang Y, 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 Surv 8–13Google Scholar
  84. 84.
    Shi XZ, Zhou J, Dong L, Hu HY, Wang HY, Chen SR (2010) Application of unascertained measurement model to prediction of classification of rockburst intensity. Chin J Rock Mech Eng 29:2720–2726Google Scholar
  85. 85.
    Qi C, Tang X (2018) Slope stability prediction using integrated metaheuristic and machine learning approaches: a comparative study. Comput Indust Eng 118:112–122Google Scholar
  86. 86.
    Konicek P (2018) Destressing. In: Feng XT (ed) Rockburst: mechanisms, monitoring, warning and mitigation. Butterworth-Heinemann, Oxford, pp 453–471Google Scholar
  87. 87.
    Gong FQ, Li XB, Zhang W (2010) Rockburst prediction of underground engineering based on Bayes discriminant analysis method. Rock Soil Mech 31(Suppl. 1):370–377Google Scholar
  88. 88.
    Ge QF, Feng XT (2008) Classification and prediction of rockburst using AdaBoost combination learning method. Rock Soil Mech 29:943–948Google Scholar
  89. 89.
    Zhao Y, Zhang Y (2008) Comparison of decision tree methods for finding active objects. Adv Space Res 41:1955–1959Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Ebrahim Ghasemi
    • 1
    Email author
  • Hasan Gholizadeh
    • 1
  • Amoussou Coffi Adoko
    • 2
  1. 1.Department of Mining EngineeringIsfahan University of TechnologyIsfahanIran
  2. 2.School of Mining and GeosciencesNazarbayev UniversityAstanaKazakhstan

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