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Engineering with Computers

, Volume 35, Issue 2, pp 659–675 | Cite as

Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques

  • Roohollah Shirani FaradonbehEmail author
  • Abbas Taheri
Original Article

Abstract

Rockburst phenomenon is the extreme release of strain energy stored in surrounding rock mass which could lead to casualties, damage to underground structures and equipment and finally endanger the economic viability of the project. Considering the complex mechanism of rockburst and a large number of factors affecting it, the conventional criteria cannot be used generally and with high reliability. Hence, there is a need to develop new models with high accuracy and ease to use in practice. This study focuses on the applicability of three novel data mining techniques including emotional neural network (ENN), gene expression programming (GEP), and decision tree-based C4.5 algorithm along with five conventional criteria to predict the occurrence of rockburst in a binary condition. To do so, a total of 134 rockburst events were compiled from various case studies and the models were established based on training datasets and input parameters of maximum tangential stress, uniaxial tensile strength, uniaxial compressive strength, and elastic energy index. The prediction strength of the constructed models was evaluated by feeding the testing datasets to the models and measuring the indices of root mean squared error (RMSE) and percentage of the successful prediction (PSP). The results showed the high accuracy and applicability of all three new models; however, the GA-ENN and the GEP methods outperformed the C4.5 method. Besides, it was found that the criterion of elastic energy index (EEI) is more accurate among other conventional criteria and with the results similar to the C4.5 model, can be used easily in practical applications. Finally, a sensitivity analysis was carried out and the maximum tangential stress was identified as the most influential parameter, which could be a guide for rockburst prediction.

Keywords

Rockburst occurrence Data mining techniques Emotional neural network Gene expression programming C4.5 algorithm Conventional criteria 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest or financial conflicts to disclose.

References

  1. 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. 2.
    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
  3. 3.
    Li N, Feng X, Jimenez R (2017) Predicting rock burst hazard with incomplete data using Bayesian networks. Tunn Undergr Sp Technol 61:61–70.  https://doi.org/10.1016/j.tust.2016.09.010 CrossRefGoogle Scholar
  4. 4.
    Weng L, Li X, Taheri A et al (2018) Fracture evolution around a cavity in brittle rock under uniaxial compression and coupled static–dynamic loads. Rock Mech Rock Eng 51(2):531–545.  https://doi.org/10.1007/s00603-017-1343-7 CrossRefGoogle Scholar
  5. 5.
    Dong LJ, Wesseloo J, Potvin Y, Li XB (2016) Discriminant models of blasts and seismic events in mine seismology. Int J Rock Mech Min Sci 86:282–291.  https://doi.org/10.1016/j.ijrmms.2016.04.021 CrossRefGoogle Scholar
  6. 6.
    Dong L, Wesseloo J, Potvin Y, Li X (2016) Discrimination of mine seismic events and blasts using the Fisher classifier, naive Bayesian classifier and logistic regression. Rock Mech Rock Eng 49:183–211.  https://doi.org/10.1007/s00603-015-0733-y CrossRefGoogle Scholar
  7. 7.
    Dong L, Shu W, Li X et al (2017) Three dimensional comprehensive analytical solutions for locating sources of sensor networks in unknown velocity mining system. IEEE Access 5:11337–11351.  https://doi.org/10.1109/ACCESS.2017.2710142 CrossRefGoogle Scholar
  8. 8.
    Dong L, Sun D, Li X, Du K (2017) Theoretical and experimental studies of localization methodology for AE and microseismic sources without pre-measured wave velocity in mines. IEEE Access 5:16818–16828.  https://doi.org/10.1109/ACCESS.2017.2743115 CrossRefGoogle Scholar
  9. 9.
    Weng L, Huang L, Taheri A, Li X (2017) Rockburst characteristics and numerical simulation based on a strain energy density index: a case study of a roadway in Linglong gold mine, China. Tunn Undergr Sp Technol 69:223–232.  https://doi.org/10.1016/j.tust.2017.05.011 CrossRefGoogle Scholar
  10. 10.
    Akdag S, Karakus M, Taheri A et al (2018) Effects of thermal damage on strain burst mechanism for brittle rocks under true-triaxial loading conditions. Rock Mech Rock Eng 51(6):1657–1682.  https://doi.org/10.1007/s00603-018-1415-3 CrossRefGoogle Scholar
  11. 11.
    He M, e Sousa LR, Miranda T, Zhu G (2015) Rockburst laboratory tests database—application of data mining techniques. Eng Geol 185:116–130.  https://doi.org/10.1016/j.enggeo.2014.12.008 CrossRefGoogle Scholar
  12. 12.
    He M, Xia H, Jia X et al (2012) Studies on classification, criteria and control of rockbursts. J Rock Mech Geotech Eng 4:97–114.  https://doi.org/10.3724/SP.J.1235.2012.00097 CrossRefGoogle Scholar
  13. 13.
    Wang J, Zeng X, Zhou J (2012) Practices on rockburst prevention and control in headrace tunnels of Jinping II hydropower station. J Rock Mech Geotech Eng 4:258–268.  https://doi.org/10.3724/SP.J.1235.2012.00258 CrossRefGoogle Scholar
  14. 14.
    Sousa R, Einstein HH (2007) Risk analysis for tunnelling projects using bayesian networks. In: 11th Congress of the International Society for Rock Mechanics, 9–13 July 2007, Lisbon, Portugal. Massachusetts Institute of Technology, pp 1301–1304Google Scholar
  15. 15.
    Jian Z, Xibing L, Xiuzhi S (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
  16. 16.
    Russenes B (1974) Analysis of Rock Spalling for Tunnels in Steep Valley Sides. Master Thesis of Science, Norwegian Institute of TechnologyGoogle Scholar
  17. 17.
    Hoek E, Brown ET (1980) Underground excavations in rock. Institution of Mining and Metallurgy, LondonGoogle Scholar
  18. 18.
    Wang YH, Li WD, Lee PKK, Tham LG (1998) Method of fuzzy comprehensive evaluations for rockburst prediction. Chin J Rock Mech Eng 17(5):493–501 (in Chinese)Google Scholar
  19. 19.
    Berthold M, Hand D (2003) Intelligent data analysis: an introduction. 2nd edn. Springer Science & Business Media, New YorkCrossRefzbMATHGoogle Scholar
  20. 20.
    Torres-Jimenez J, Rodriguez-Cristerna A (2017) Metaheuristic post-optimization of the NIST repository of covering arrays. CAAI Trans Intell Technol 2:31–38.  https://doi.org/10.1016/j.trit.2016.12.006 CrossRefGoogle Scholar
  21. 21.
    Khandelwal M, Shirani Faradonbeh R, Monjezi M et al (2017) Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models. Eng Comput 33:13–21.  https://doi.org/10.1007/s00366-016-0452-3 CrossRefGoogle Scholar
  22. 22.
    Aryafar A, Mikaeil R, Haghshenas SS, Haghshenas SS (2018) Application of metaheuristic algorithms to optimal clustering of sawing machine vibration. Meas J Int Meas Confed 124:20–31.  https://doi.org/10.1016/j.measurement.2018.03.056 CrossRefGoogle Scholar
  23. 23.
    Mikaeil R, Haghshenas SS, Hoseinie SH (2018) Rock penetrability classification using artificial bee colony (ABC) algorithm and self-organizing map. Geotech Geol Eng 36:1309–1318.  https://doi.org/10.1007/s10706-017-0394-6 Google Scholar
  24. 24.
    Feng X, Wang L (1994) Rockburst prediction based on neural networks. Trans Nonferrous Met Soc China 4(1):7–14Google Scholar
  25. 25.
    Zhao HB (2005) Classification of rockburst using support vector machine. Rock Soil Mech 26(4):642–644 (in Chinese)MathSciNetGoogle Scholar
  26. 26.
    Gong FQ, Li XB (2007) A distance discriminant analysis method for prediction of possibility and classification of rockburst and its application. Chin J Rock Mech Eng 26(5):1012–1018 (in Chinese) Google Scholar
  27. 27.
    Shi XZ, Zhou J, Dong L et al (2010) Application of unascertained measurement model to prediction of classification of rockburst intensity. Chin J Rock Mech Eng 29:2720–2726Google Scholar
  28. 28.
    Zhou J, Shi XZ, Dong L et al (2010) Fisher discriminant analysis model and its application for prediction of classification of rockburst in deepburied long tunnel. J Coal Sci Eng 16(2):144–149CrossRefGoogle Scholar
  29. 29.
    Palmstrom A (1995) Characterizing the strength of rock masses for use in design of underground structures. In: International conference in design and construction of underground structures, p 10Google Scholar
  30. 30.
    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 68:549–568.  https://doi.org/10.1007/s11069-013-0635-9 CrossRefGoogle Scholar
  31. 31.
    Dancy C, Reidy J (2004) Statistics without maths for psychology. Pearson Education Limited, New YorkGoogle Scholar
  32. 32.
    Middleton GV (2000) Data analysis in the earth sciences using MATLAB®. Prentice Hall, Englewood CliffsGoogle Scholar
  33. 33.
    Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 99:51–60.  https://doi.org/10.1016/j.enggeo.2008.02.003 CrossRefGoogle Scholar
  34. 34.
    Yang H, Yu L (2017) Feature extraction of wood-hole defects using wavelet-based ultrasonic testing. J For Res 28:395–402.  https://doi.org/10.1007/s11676-016-0297-z CrossRefGoogle Scholar
  35. 35.
    Faradonbeh RS, Monjezi M (2017) Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms. Eng Comput 33:835–851CrossRefGoogle Scholar
  36. 36.
    McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133.  https://doi.org/10.1007/BF02478259 MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Tracewski L, Bastin L, Fonte CC (2017) Repurposing a deep learning network to filter and classify volunteered photographs for land cover and land use characterization. Geo-Spatial Inf Sci 20:252–268.  https://doi.org/10.1080/10095020.2017.1373955 CrossRefGoogle Scholar
  38. 38.
    Guo K, Wu S, Xu Y (2017) Face recognition using both visible light image and near-infrared image and a deep network. CAAI Trans Intell Technol 2:39–47.  https://doi.org/10.1016/j.trit.2017.03.001 CrossRefGoogle Scholar
  39. 39.
    Mohamad ET, Faradonbeh RS, Armaghani DJ et al (2016) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2359-8 Google Scholar
  40. 40.
    Mikaeil R, Haghshenas SS, Ozcelik Y, Gharehgheshlagh HH (2018) Performance evaluation of adaptive neuro-fuzzy inference system and group method of data handling-type neural network for estimating wear rate of diamond wire saw. Geotech Geol Eng.  https://doi.org/10.1007/s10706-018-0571-2 Google Scholar
  41. 41.
    Lotfi E, Akbarzadeh-T MR (2014) Practical emotional neural networks. Neural Netw 59:61–72.  https://doi.org/10.1016/j.neunet.2014.06.012 CrossRefGoogle Scholar
  42. 42.
    Lotfi E, Akbarzadeh- TMR (2016) A winner-take-all approach to emotional neural networks with universal approximation property. Inf Sci 346–347:369–388.  https://doi.org/10.1016/j.ins.2016.01.055 CrossRefGoogle Scholar
  43. 43.
    Lotfi E, Khosravi A, Akbarzadeh-T MR, Nahavandi S (2014) Wind power forecasting using emotional neural networks. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp 311–316Google Scholar
  44. 44.
    Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC press, Boca RatonzbMATHGoogle Scholar
  45. 45.
    Salimi A, Faradonbeh RS, Monjezi M, Moormann C (2016) TBM performance estimation using a classification and regression tree (CART) technique. Bull Eng Geol Environ.  https://doi.org/10.1007/s10064-016-0969-0 Google Scholar
  46. 46.
    Hasanipanah M, Faradonbeh RS, Armaghani DJ et al (2017) Development of a precise model for prediction of blast-induced flyrock using regression tree technique. Environ Earth Sci.  https://doi.org/10.1007/s12665-016-6335-5 Google Scholar
  47. 47.
    Coimbra R, Rodriguez-Galiano V, Olóriz F, Chica-Olmo M (2014) Regression trees for modeling geochemical data: an application to Late Jurassic carbonates (Ammonitico Rosso). Comput Geosci 73:198–207.  https://doi.org/10.1016/j.cageo.2014.09.007 CrossRefGoogle Scholar
  48. 48.
    Jahed Armaghani D, Mohd Amin MF, Yagiz S et al (2016) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci.  https://doi.org/10.1016/j.ijrmms.2016.03.018 Google Scholar
  49. 49.
    Liang M, Mohamad ET, Faradonbeh RS et al (2016) Rock strength assessment based on regression tree technique. Eng Comput.  https://doi.org/10.1007/s00366-015-0429-7 Google Scholar
  50. 50.
    Hasanipanah M, Faradonbeh RS, Amnieh HB et al (2017) Forecasting blast-induced ground vibration developing a CART model. Eng Comput.  https://doi.org/10.1007/s00366-016-0475-9 Google Scholar
  51. 51.
    Quinlan JR (1993) C4.5: Programs for machine learning. Elsevier, AmsterdamGoogle Scholar
  52. 52.
    Ghasemi E, Kalhori H, Bagherpour R (2017) Stability assessment of hard rock pillars using two intelligent classification techniques: a comparative study. Tunn Undergr Sp Technol 68:32–37.  https://doi.org/10.1016/j.tust.2017.05.012 CrossRefGoogle Scholar
  53. 53.
    Hssina B, Merbouha A, Ezzikouri H, Erritali M (2014) A comparative study of decision tree ID3 and C4.5. Int J Adv Comput Sci Appl 4(2):13–19Google Scholar
  54. 54.
    Ture M, Tokatli F, Kurt I (2009) Using Kaplan-Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patients. Expert Syst Appl 36:2017–2026.  https://doi.org/10.1016/j.eswa.2007.12.002 CrossRefGoogle Scholar
  55. 55.
    Bui DT, Pradhan B, Lofman O, Revhaug I (2012) Landslide Susceptibility assessment in Vietnam using support vector machines, decision tree, and naive Bayes models. Math Probl Eng.  https://doi.org/10.1155/2012/974638 Google Scholar
  56. 56.
    Ferreira C (2002) Gene expression programming in problem solving. In: Roy R, Köppen M, Ovaska S et al (eds) Soft computing and industry: recent applications. Springer, London, pp 635–653CrossRefGoogle Scholar
  57. 57.
    Güllü H (2012) Prediction of peak ground acceleration by genetic expression programming and regression: a comparison using likelihood-based measure. Eng Geol 141–142:92–113.  https://doi.org/10.1016/j.enggeo.2012.05.010 CrossRefGoogle Scholar
  58. 58.
    Armaghani DJ, Faradonbeh RS, Rezaei H et al (2016) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2618-8 Google Scholar
  59. 59.
    Faradonbeh RS, Armaghani DJ, Amnieh HB, Mohamad ET (2016) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2537-8 Google Scholar
  60. 60.
    Faradonbeh RS, Hasanipanah M, Amnieh HB et al (2018) Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environ Monit Assess.  https://doi.org/10.1007/s10661-018-6719-y Google Scholar
  61. 61.
    Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer, New YorkCrossRefzbMATHGoogle Scholar
  62. 62.
    Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38:4080–4087.  https://doi.org/10.1016/j.eswa.2010.09.071 CrossRefGoogle Scholar
  63. 63.
    Khandelwal M, Armaghani DJ, Faradonbeh RS et al (2016) A new model based on gene expression programming to estimate air flow in a single rock joint. Environ Earth Sci.  https://doi.org/10.1007/s12665-016-5524-6 Google Scholar
  64. 64.
    Zhang JF (2007) Study on Prediction by Stages and Control Technology of Rockburst Hazard of Daxiangling Highway Tunnel. M.Sc. Thesis, Southwest Jiaotong University, ChenduGoogle Scholar
  65. 65.
    Yang JL, Li XB, Zhou ZL, Lin Y (2010) A Fuzzy assessment method of rock-burst prediction based on rough set theory. Met Mine 6:26–29 (in Chinese) Google Scholar
  66. 66.
    Zhang LX, Li CH (2009) Study on tendency analysis of rockburst and comprehensive prediction of different types of surrounding rock. Tang CA (ed), Proc 13th Int Symp Rockburst Seism Mines Rint Press Dalian, pp 1451–1456Google Scholar
  67. 67.
    Yi YL, Cao P, Pu CZ (2010) Multi-factorial comprehensive estimation for jinchuan’s deep typical rockburst tendency. Sci Technol Rev 28:76–80Google Scholar
  68. 68.
    Kamari A, Arabloo M, Shokrollahi A et al (2015) Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO2 flooding. Fuel 153:310–319.  https://doi.org/10.1016/j.fuel.2015.02.087 CrossRefGoogle Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Civil, Environmental and Mining EngineeringThe University of AdelaideAdelaideAustralia

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