Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines

Safety Science

Volumn 50, Issue 4, 2012, Pages 629-644

  Zhou, Jian ^a   Li, Xibing ^a   Shi, Xiuzhi ^a,b  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b Postdoctoral Workstation of Daye Nonferrous Metals Company   (China)

Author keywords

Classification; Genetic algorithm (GA); Particle swarm optimization algorithm (PSO); Prediction; Rockburst; Support vector machines (SVMs); Underground openings

Indexed keywords

BURIED DEPTH; CLASSIFICATION TECHNIQUE; DATA SETS; DESIGN AND CONSTRUCTION; ELASTIC ENERGY; EXPERIMENTAL VALUES; GENERALIZATION ABILITY; GRID-SEARCH METHOD; HAZARD ASSESSMENT; HIGH POTENTIAL; HYBRID MODEL; KERNEL FUNCTION; LONG-TERM PREDICTION; MAXIMUM TANGENTIAL STRESS; MULTI VARIABLES; PARAMETER OPTIMIZATION; PARTICLE SWARM OPTIMIZATION ALGORITHM; PARTICLE SWARM OPTIMIZATION ALGORITHM (PSO); POSSIBILITY PREDICTION; PREDICTIVE ACCURACY; RADIAL BASIS FUNCTIONS; ROBUST MODELS; ROCK BRITTLENESS; ROCKBURST PREDICTION; STATISTICAL LEARNING; STRESS COEFFICIENTS; STRONG COUPLING; STRONG INTERFERENCE; SUPPORT VECTOR; UNDERGROUND OPENING; UNIAXIAL COMPRESSIVE STRENGTH; UNIAXIAL TENSILE STRENGTH;

CLASSIFICATION (OF INFORMATION); COMPRESSIVE STRENGTH; COUPLINGS; DATA PROCESSING; FORECASTING; FRACTURE MECHANICS; GENETIC ALGORITHMS; HEURISTIC METHODS; LEARNING ALGORITHMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PARTICLE SWARM OPTIMIZATION (PSO); RADIAL BASIS FUNCTION NETWORKS; ROCK BURSTS; ROCKS; SUPPORT VECTOR MACHINES; TENSILE STRENGTH; UNDERGROUND STRUCTURES; VECTORS;

HEURISTIC ALGORITHMS;

ACCURACY; ARTICLE; CALCULATION; GENETIC ALGORITHM; INTERMETHOD COMPARISON; KERNEL METHOD; LEARNING ALGORITHM; PREDICTION; PRIORITY JOURNAL; ROCK; ROCKBURST; SUPPORT VECTOR MACHINE; VALIDITY;

EID: 84856475059     PISSN: 09257535     EISSN: 18791042     Source Type: Journal    
DOI: 10.1016/j.ssci.2011.08.065     Document Type: Article

References (83)

1. Alcott J.M., Kaiser P.K., Simser B.P. Use of microseismic source parameters for rockburst hazard assessment. Pure and Applied Geophysics 1998, 153:41-65.
2. Bai M.Z., Wang L.J., Xu Z.Y. Study on a neutral network model and its application in predicting the risk of rock burst. China Safety Science Journal 2002, 12(4):65-69. (in Chinese).
3. Board M., Fairhurst C. Rockbursts: Prediction and Control 1983, Institution of Mining and Metallurgy, London.
4. Brady B.T., Leighton F.W. Seismicity anomaly prior to a moderate rock burst - case study. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts 1977, 14(3):127-132.
5. Casten U., Fajklewicz Z. Induced gravity-anomalies and rockburst risk in coal-mines - a case history. Geophysical Prospecting 1993, 41(1):1-13.
6. Chang, C.C., Lin, C.J., 2001. LIBSVM: a Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
7. Chen H.J., Li N.H., Ni D.X., Shang Y.Q. Prediction of rockburst by artificial neural network. Chinese Journal of Rock Mechanics and Engineering 2003, 22(5):762-768.
8. Cook N.G.W., Hoek E., Pretorius J.P.G., Ortlepp W.D., Salamon M.D.G. Rock mechanics applied to the study of rockbursts. Journal of the South African Institute of Mining and Metallurgy 1966, 66(3):435-528.
9. Chen X., Sun J.Z., Zhang J.K., Chen Q.S. Judgment indexes and classification criteria of rock burst with the extension judgment method. China Civil Engineering Journal 2009, 42(9):82-88. (in Chinese).
10. Crrp, 1996. A Comprehensive Summary of Five Years of Collaborative Research on Rockbursting in Hard Rock Mines. CAMIRO Mining Division, Canadian Rockburst Research Program, Book 1.
11. Deng L., Lv Y., Deng R.G. Present situation and consideration of rock burst hazard. Advanced Materials Research 2011, 136-147.
12. Dowding C.H., Andersson C.A. Potential for rock bursting and slabbing in deep caverns. Engineering Geology 1986, 22:265-279.
13. Feng X.T., Wang L.N. Rockburst prediction based on neural networks. Transactions of Nonferrous Metals Society of China 1994, 4(1):7-14.
14. Frid V. Rockburst hazard forecast by electromagnetic radiation excited by rock fracture. Rock Mechanics and Rock Engineering 1997, 30(4):229-236.
15. Goldberg G.E. Genetic Algorithms in Search, Optimization and Machine Learning 1989, Addison-Wesley, NY, USA.
16. Gong F.Q., Li X.B. A distance discriminant analysis method for prediction of possibility and classification of rockburst and its application. Chinese Journal of Rock Mechanics and Engineering 2007, 26(5):1012-1018. (in Chinese).
17. Gunyon I., Weston J., Barnhill S., Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning 2002, 46(1-3):389-422.
18. Gunn, S.R., 1998. Support Vector Machines for Classification and Regression. Technical Report, University of Southampton, Department of Electrical and Computer Science, UK. http://www.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf.
19. Guyon, I., Christianini, N., 1999. Survey of support vector machine applications. In: Proceedings of NIPS'99 Special Workshop on Learning with Support Vector. http://www.clopinet.com/isabelle/Projects/SVM/applist.html.
20. He M.C., Miao J.L., Feng J.L. Rock burst process of limestone and its acoustic emission characteristics under true-triaxial unloading conditions. International Journal of Rock Mechanics & Mining Sciences 2010, 47(2):286-298.
21. Hoek E., Brown E.T. Practical estimates of rock mass strength. International Journal of Rock Mechanics and Mining Sciences 1997, 34:1165-1186.
22. Hou, F.L., 1989. Criterion and prevention measures on rockburst in circular tunnel. In: Chinese Society for Rock Mechanics and Engineering. Proceedings of the 2th National Rock Mechanics and Engineering. Knowledge Press, Beijing, pp. 195-201 (in Chinese).
23. Hucka V., Das B. Brittleness determination of rocks by different methods. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts 1974, 11(10):389-392.
24. Jha P.C., Chouhan R.K.S. Long range rockburst prediction: a seismological approach. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 1994, 31(1):71-77.
25. Jiang A.N., Wang S.Y., Tang S.L. Feedback analysis of tunnel construction using a hybrid arithmetic based on support vector machine and particle swarm optimisation. Automation in Construction 2011, 20(4):482-489.
26. Jiang Q., Feng X.T., Xiang T.B., Su G.S. Rockburst characteristics and numerical simulation based on a new energy index: a case study of a tunnel at 2500 m depth. Bulletin of Engineering Geology and the Environment 2010, 69:381-388.
27. Kang, Y., 2006. Research on Relevant Probloms about Failure Mechnism of Surrounding Rock in Deep Buried Tunnel. Ph.D. Dissertation. Chongqing University, Chongqing, pp. 118-120 (in Chinese).
28. Kaiser, P.K., McCreat, D.R., Tannnant, D.D., 1996. In: Canadian Rockburst Support Handbook. Geomechanics Research Centre.
29. Kennedy, J., Eberhart, R.C., 1995. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Network. vol. 4, IEEE Press, New York, pp, 1942-1948.
30. Khandelwal M. Evaluation and prediction of blast-induced ground vibration using support vector machine. International Journal of Rock Mechanics and Mining Sciences 2010, 47(3):509-516.
31. Kidybinski A. Bursting liability indices of coal. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts 1981, 18(4):295-304.
32. Kovačević M., Bajat B., Gajić B. Soil type classification and estimation of soil properties using support vector machines. Geoderma 2010, 154(3-4):340-347.
33. Li, Y., 2009. LIBSVM-farutoUltimate Version: a Toolbox with Implements for Support Vector Machines based on Libsvm. http://www.ilovematlab.cn.
34. Liang, Z.Y., 2004. Study on the Prediction and Prevention of Rockburst in the Diversion Tunnel of Jinping II Hydropower Station, M.Sc. Thesis. Chengdu University of Technology, Chendu, pp. 61-62 (in Chinese).
35. Linkov A.M. Rockbursts and the instability of rock masses. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts 1996, 33(7):727-732.
36. Liu Z.J., Yuan Q.P., Li J.L. Application of fuzzy probability model to prediction of classification of rockburst intensity. Chinese Journal of Rock Mechanics and Engineering 2008, 27(Supp. 1):3095-3103. (in Chinese).
37. Mansurov V.A. Prediction of rockbursts by analysis of induced seismicity data. International Journal of Rock Mechanics & Mining Sciences 2001, 38(7):893-901.
38. Matlab 7.0. Program, Available from The Mathworks Inc., Natick, MA. http://www.mathworks.com.
39. Mib S.H., Lee J.M., Han I.G. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications 2006, 31(3):652-660.
40. Neyman, B., Szecowka, Z., Zuberek, W., 1972. Effective methods for fighting rock burst in Polish collieries. In: Proceedings of the 5th International Strata Control Conference, vol. 23, pp. 1-9.
41. Ortlepp W.D. Rock fracture and rockbursts-an illustrative study. SAIMM Monograph Series 1997, Johannesburg, Berlin, M9.
42. Ortlepp W.D., Stacey T.R. Rockburst mechanisms in tunnels and shafts. Tunnelling and Underground Space Technology 1994, 9(1):59-65.
43. Pan, Y.S., Li, Z.H. 2002. The analytic analysis stability of rock structure in mine. In: Proceedings of National Conference on Solid, Mechanics, Dalian University of Technology Press, Dalian (in Chinese).
44. Pan Y., Zhang Y., Yu G.M. Mechanism and catastrophe theory analysis of circular tunnel rockburst. Applied Mathematics and Mechanics 2006, 27(6):841-852. (in Chinese).
45. Peng Q., Qian A.G., Xiao Y. Research on prediction system for rockburst based on artificial intelligence application methods. Journal of Sichuan University: Engineering Science Edition 2010, 42(4):18-24. (in Chinese).
46. Qin, S.W., Chen, J.P., Wang, Q., 2009. Research on rockburst prediction with extenics evaluation based on rough set. In: Tang, C.A. (Ed.), Proceedings of the 13th International Symposium on Rockburst and Seismicity in Mines. Rinton Press, Dalian, pp. 937-944.
47. Ren Y., Bai G.C. Determination of optimal SVM parameters by using GA/PSO. Journal of Computers 2010, 5(8):1160-1168.
48. Russenes, B.F., 1974, Analysis of Rock Spalling for Tunnels in Steep Valley Sides (in Norwegian), M.Sc. thesis. Norwegian Institute of Technology, Department of Geology, p. 247.
49. Ryder J.A. Excess shear stress in the assessment of geologically hazardous situations. Journal of the South African Institute of Mining and Metallurgy 1987, 88(1):27-39.
50. Sharan S.K. A finite element perturbation method for the prediction of rockburst. Computers and Structures 2004, 85:1304-1309.
51. Shi X.Z., Zhou J., Dong L., Hu H.Y., Wang H.Y., Chen S.R. Application of unascertained measurement model to prediction of classification of rockburst intensity. Chinese Journal of Rock Mechanics and Engineering 2010, 29(supp.1):2720-2727. (in Chinese).
52. Shivakumar K., Rao M.V.M.S., Srinivasan C., Kusunose K. Multifractal analysis of the spatial distribution of area rockbursts at Kolar Gold Mines. International Journal of Rock Mechanics & Mining Sciences 1996, 33(2):167-172.
53. Singh S.P. Burst energy release index. Rock Mechanics and Rock Engineering 1988, 21(1):149-155.
54. Su, G.S., Zhang, Y., Chen, G.Q., 2010. Identify rockburst grades for jinping hydropower station using gaussian II process for binary classification. In: Proceedings of 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE 2010), vol. 2, Changchun, pp. 364-367.
55. Sun J., Wang S.J. Rock mechanics and rock engineering in China: developments and current state-of-the-art. International Journal of Rock Mechanics and Mining Sciences 2000, 37(3):447-465.
56. Tao, Z.Y., 1988. Support design of tunnels subjected to rockbursting. In: Romana (Ed.), ISRM International Symposium, Rock Mechanics and Power Plants, pp. 407-411.
57. Turchaninov I.A., Markov G.A., Gzovsky M.V., Kazikayev D.M., Frenze U.K., Batugin S.A., Chabdarova U.I. State of stress in the upper part of the Earth's crust based on direct measurements in mines and on tectonophysical and seismological studies. Physics of the Earth and Planetary Interiors 1972, (6):229-234.
58. Tang, B.Y., 2000. Rockburst Control using Distress Blasting, Ph.D. Dissertation. McGill University, Montreal, Canada.
59. Tang C.A., Wang J.M., Zhang J.J. Preliminary engineering application of microseismic monitoring technique. Journal of Rock Mechanics and Geotechnical Engineering 2010, 2(3):193-208.
60. Tang L.Z., Wang W.X. New rock burst proneness index. Chinese Journal of Rock Mechanics and Engineering 2002, 21(6):874-878. (in Chinese).
61. Tang L.Z., Xia K.W. Seismological method for prediction of areal rockbursts in deep mine with seismic source mechanism and unstable failure theory. Journal of central south university of technology: English edition 2010, 17:947-953.
62. Vapnik V. The Nature of Statistical Learning Theory 1995, Springer-Verlag, New York.
63. Wang Y.C., Shang Y.Q., Sun H.Y., Yan X.S. Study of prediction of rockburst intensity based on efficacy coefficient method. Rock and Soil Mechanics 2010, 31(2):529-534. (in Chinese).
64. Wang Y.H., Li W.D., Li Q.G., Xu Y., Tan G.H. Method of fuzzy comprehensive evaluations for rockburst prediction. Chinese Journal of Rock Mechanics and Engineering 1998, 17(5):493-501. (in Chinese).
65. Wang J.A., Park H.D. Comprehensive prediction of rockburst based on analysis of strain energy in rocks. Tunnelling and Underground Space Technology 2001, 16(1):49-57.
66. Wang, X.F., Li X.H., Gu, Y.L., Jin, X.G., Kang, Y., Li, D.X., 2004. Application of BP neural network into prediction of rockburst in tunneling. In: Proceedings of the 2004 International Symposiumon Safety Science and Technology, vol. 4, China Science Press, Shanghai, pp. 617-621.
67. Wu, Y.K., Zhang, W.B., 1997. Prevention of rockbursts in coal mines in China. In: Gibowicz S.J., Lasocki, J. (Eds.), Proceedings of the 4th International Symposium on Rockbursts and Seismicity in Mines, Rotterdam, pp. 361-366.
68. Xie H.P., Pariseau W.G. Fractal character and mechanism of rock bursts. Chinese Journal of Rock Mechanics and Engineering 1993, 12(1):28-37. (in Chinese).
69. Xu M.G., Du Z.J., Yao G.H., Liu Z.P. Rockburst prediction of chengchao iron mine during deep mining. Chinese Journal of Rock Mechanics and Engineering 2008, 27(supp.1):2921-2928. (in Chinese).
70. Yang J.L., Li X.B., Zhou Z.L., Lin Y. A Fuzzy assessment method of rock-burst prediction based on rough set theory. Metal Mine 2010, (6):26-29. (in Chinese).
71. Yi Y.L., Cao P., Pu C.Z. Multi-factorial comprehensive estimation for jinchuan's deep typical rockburst tendency. Science & Technology Review 2010, 28(2):76-80. (in Chinese).
72. Zhang H., Wang Y.J., Li Y.F. SVM model for estimating the parameters of the probability integral method of predicting mining subsidence. Mining Science and Technology 2009, 19(2):385-388.
73. Zhang, L.X, Li, C.H., 2009. Study on tendency analysis of rockburst and comprehensive prediction of different types of surrounding rock. In: Tang, C.A. (Ed.), Proceedings of the 13th International Symposium on Rockburst and Seismicity in Mines. Rinton Press, Dalian, pp. 1451-1456.
74. Zhang J.J., Fu B.J. Rockburst and its criteria and control. Chinese Journal of Rock Mechanics and Engineering 2008, 27(10):2034-2042. (in Chinese).
75. Zhang, J.F., 2007. Study on Prediction by Stages and Control Technology of Rockburst Hazard of Daxiangling Highway Tunnel. M.Sc. Thesis, Southwest Jiaotong University, Chendu (in Chinese).
76. Zhang L.W., Zhang D.Y., Qiu D.H. Application of extension evaluation method in rockburst prediction based on rough set theory. Journal of China Coal Society 2010, 35(9):1461-1465. (in Chinese).
77. Zhang Z.Y., Song J.B., Li P.F. Rock burst comprehensive forecasting method for the chamber group of underground power house. Advance in Earth Sciences 2004, 19(3):451-456. (in Chinese).
78. Zhao H.B. Classification of rockburst using support vector machine. Rock and Soil Mechanics 2005, 26(4):642-644. (in Chinese).
79. Zhao H.B., Yin S.D. Geomechanical parameters identification by particle swarm optimization and support vector machine. Applied Mathematical Modelling 2009, 33(10):3997-4012.
80. Zhou J., Shi X.Z., Dong L., Hu H.Y., Wang H.Y. Fisher discriminant analysis model and its application for prediction of classification of rockburst in deep-buried long tunnel. Journal of Coal Science and Engineering (China) 2010, 16(2):144-149.
81. Zhou X., Li Z.C., Dai Z., Zou X.Y. QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm. Journal of Molecular Graphics and Modelling 2010, 29(2):188-196.
82. Zhu W.C., Li Z.H., Zhu L., Tang C.A. Numerical simulation on rockburst of underground opening triggered by dynamic disturbance. Tunnelling and Underground Space Technology 2010, 25:587-599.
83. Zubelewicz A., Mroz Z. Numerical-simulation of rock burst processes treaded as problems of dynamic instability. Rock Mechanics and Rock Engineering 1983, 16(4):253-274.