Prediction of Classification of Rock Burst Risk Based on Genetic Algorithms with SVM


Article Preview

Due to the complex features of rock burst hazard assessment systems, a support vector machine (SVM) model for predicting of classification of rock burst was established based on the SVM theory and the actual characteristics of the project in this study. The main factors of rock burst, such as coal seam, dip, buried depth, structure situation, change of pitch angle, change of coal thickness, gas concentration, roof management, pressure relief and shooting were defined as the criterion indices for rock burst prediction in the proposed model. In order to determine reasonable and efficient the parameters of SVM, Firstly, the appropriate fitness function for genetic algorithms (GA) operation was determined, and then optimization parameters of SVM model were selected by real coded GA, therefore, the genetic algorithms and support vector machine (GSVM) model was established. A GSVM model was obtained through training 23 sets of measured data, the cross-validation method was introduced to verify the stability of GSVM model and the ratio of mis-discrimination is 0. Moreover, the proposed model was used to predict 12 new samples rock burst, the correct rate of prediction results is 91.6667% and are identical with actual situation. The results show that the genetic algorithm can speed up SVM parameter optimization search, the proposed model has a high credibility in the study of rock burst prediction of risk classification, which can be applied to practical engineering.



Edited by:

Fangping Zhang




Y. H. Peng et al., "Prediction of Classification of Rock Burst Risk Based on Genetic Algorithms with SVM", Applied Mechanics and Materials, Vol. 628, pp. 383-389, 2014

Online since:

September 2014




* - Corresponding Author

[1] W. D. Ortlepp and T. R. Stacey. Rockburst mechanisms in tunnels and shafts[J]. Tunnelling and Underground Space Technology, 9(1): 59-65, (1994).


[2] V. Frid. Calculation of electromagnetic radiation criterion for rockburst hazard forecast in coal mines[J]. Pure and Applied Geophysics, 158(5/6): 931-944, (2001).


[3] Mansurov, V.A. 2001. Prediction of rockbursts by analysis of induced seismicity data[J]. International Journal of Rock Mechanics & Mining Sciences, 38 (7): pp.893-901.


[4] G. F. Dai. Research on nonlinear dynamics characteristics of rock and rockburst in coal mine [Doctor thesis]. Chongqing: Chongqing University, (2002).

[5] J. A Wang and H. D. Park. Comprehensive prediction of rockburst based on analysis of strain energy in rocks[J]. Tunnelling and Underground Space Technolog (16): 49–57, (2001).


[6] W. Li, H. G. Ji and Y. L. Wu. Mechanism analyses and forecast on deep mining rock burst[J]. China Mining Magazine. 16(7): 105–107, (2007).

[7] G. X. Chen, L. M. Dou, A. Y. Cao and Z. H. Li. Assessment of rock burst danger and application on electromagnetic emission method[J]. Journal of China Coal Society, 33(8): 866–870, (2008).

[8] S. K. Sharar. A finite element perturbation method for the prediction of rockburst,. Computers and Structures, 85 (1): 1304-1309, (2007).


[9] G. Z. Yin, Q. W. Tan, Z. A. Wei. Combined optimization model of rock-burst prediction based on chaos optimization and BP neural networks[J]. Journal of China Coal Society, 33(8): 871–875, (2008).

[10] X. Z. Shi, J. Zhou, L. Dong,H. Y. Hu, H. Y. Wang and S. R. Chen. Application of unascertained measurement model to prediction of classification of rockburst intensity[J]. Chinese Journal of Rock Mechanics and Engineering, 29(supp. 1): 2720-2727, (2010).

[11] F. Q. Sun. New rock burst prediction modeling based on ensemble neural network[J]. Journal of Jilin University(Information Science Edition), 27(1): 79–84, (2009).

[12] J. Zhou, X. Z. Shi, L. Dong, H. Y. Hu and H. Y. Wang. Fisher discriminant analysis model and its application for prediction of classification of rockburst in deep-buried long tunnel[J]. Journal of Coal Science & Engineering(China), 16(2): 144–149, (2010).


[13] V. Vapnik. The nature of statistical learning theory[M]. Springer-Verlag, New York, (1995).

[14] C. C. Chang and C. J. Lin. LIBSVM: a library for support vector machines, (2001).

[15] J. Zhou, X. B. Li, X. Z. Shi, W. Wei and B. B. Wu. Predicting pillar stability for underground mine using Fisher discriminant analysis and SVM methods[J]. Trans. Nonferrous Met. Soc. China, 21(12): 2734−2743, (2011).


[16] J. Zhou, X. B. Li, X. Z. Shi. Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines[J]. Safety Science, 2012, 50(4), 629-644.


[17] C. H. Wu, G. H. Tzeng, Y. J. Goo and W. C. Fang. A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy[J]. Expert Systems with Applications, 32: 397–408, (2007).