NS  Vol.4 No.2 , February 2012
Deformation prediction model of surrounding rock based on GA-LSSVM-markov
ABSTRACT
Command protection engineering is the important component of national protection engineering system. To raise the level of its construction, a deformation prediction model is given based on Genetic Algorithm (GA), Least Square Support Vector Machines (LSSVM) and markov theory. Genetic algorithm is used to improve the parameter of LSSVM. Markov predict method is used to improve the precision of the prediction model. Finally, be used to a certain command protection engineering, the accuracy of the algorithm is improved obviously. The model is proved to be credible and precise.

Cite this paper
Wang, D. , Qiu, G. , Xie, W. and Wang, Y. (2012) Deformation prediction model of surrounding rock based on GA-LSSVM-markov. Natural Science, 4, 85-90. doi: 10.4236/ns.2012.42013.
References
[1]   Guo, W. (2009) Prediction of rock deformation and analysis of Rock Stability of Tunnel Based on the Neural Network. Chongqing University, Chongqing.

[2]   Feng, X.-T. (2000) Intelligent rock mechanics. Science Press, Beijing.

[3]   Zhao, P. (2009) Prediction for surrounding rock deformation of tunnel based on SVM coupling with cyclic variable method. Journal of Shijiazhuang Railway Institute (Natural Science), 22, 61-64.

[4]   Wang, Y.-Y. (2001) Analysis, control and forecast of deformation and pressure in soft rock tunnel. Liaoning Engineering Technology University, Fuxin.

[5]   Wu, Y.-P. and Li, Y.-W. (2008) Application of grey-ENN model to prediction of wall-rock deformation in deep buried tunnels. Rock and Soil Mechanics, 29, 263-266.

[6]   Jiang, Y.-N., Feng, X.-T. and Gao, W. (2002) Study of integration intelligence for constringency displacement analyzing of large cavern group. Chinese Journal of Rock Mechanics and Engineering, 25, 2501-2505.

[7]   Gong, K.-Y. (2004) The application of gray system theory in the roadway tunneling and surrounding rock deformation prediction. Shandong University, Jinan.

[8]   Li, S.-C., Wang, W.-M. and Wang, L.-C. (1997) Application of non linear time series analysis model to displacement forecasting in underground engineering. Chinese Journal of Geotechnical Engineering, 19, 15-20.

[9]   Zhang, Z.-Q., Feng, X.-T. and Yang, C.-X. (1999) Study on applicability of genetic-neural network modeling of nonlinear displacement time series. Rock and Soil Mechanics, 16, 20-24.

[10]   Gao, W. and Zheng, Y.-R. (2003) Back analysis in geotechnical mechanics and its integrated intelligent study. Rock and Soil Mechanics, 37, 114-116.

[11]   Vapnik, V. (1995) The nature of statistical learning theory. Spring Verlag.

[12]   Zhao, H.-B. (2005) Predicting the surrounding deformations of tunnel using support vector machine. Chinese Journal of Rock Mechanics and Engineering, 4, 649-652.

[13]   Thissen, U., Van Brakel, R. and de Weijer, A.P. (2003) Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems, 69, 35-49. doi:10.1016/S0169-7439(03)00111-4

[14]   Wang, K.-Q., Yang, S.-C. and Dai, T.-H. (2009) Method of optimizing parameter of least squares support vector machines by genetic algorithm. Computer Applications and Software, 26, 649-652.

[15]   Liu, J.-F. and Li, X.-W. (2000) The stochastic process. China Railway Publishing House, Beijing.

[16]   Zhang, F.-M. and Cui, G.-L. (2006) The settlement prediction of foundation pit based on gray markov chain model. Soil Engineering and Foundation, 16, 82-84, 93.

[17]   Xu, F. and Xu, W.-Y. (2010) Prediction of displacement time series based on support vector machines-Markov chain, Rock and Soil Mechanics, 31, 944-948.

 
 
Top