Applying Support Vector Machines to Predict Tunnel Surrounding Rock Displacement

Abstract:

Displacement prediction of tunnel surrounding rock plays a significant role for safety estimation during tunnel construction. This paper presents an approach to use support vector machines (SVM) to predict tunnel surrounding rock displacement. A stepwise search is also introduced to optimize the parameters in SVM. The data of Fangtianchong tunnel is use to evaluate the proposed model. The comparison between artificial neural network (ANN) and SVM shows that SVM has a high-accuracy prediction than ANN. Results also show SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.

• Periodical: Applied Mechanics and Materials (Volumes 29-32)
• Pages: 1717-1721
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.29-32.1717
• Online since: August 2010
• Authors: Bao Zhen Yao, Cheng Yong Yang, Bing Yu, Fang Fang Jia, Bo Yu
• Keywords: Prediction, Stepwise Search, Support Vector Machine (SVM), Surrounding Rock Displacement, Tunnel

References

[1] Xiao, W. Q and Leng, W.M. (2004); The Dynamic Displacement Prediction for the Retaining Structure of the Deep Excavation Pit in Subway. CHINA RAILWAY SCIENCE, 25(5): 84-88.

[2] Zhu, Y.Q., Zhang, S.M. and Jing, S.T. (2005); Concept and Determination of Limit Displacements of Primary Support System of Railway Tunnel. Chinese Journal of Rock Mechanics and Engineering, 24(9): 1594-1598.

[3] Zhang, C.L., Wang, X.W., Chen, H. (2008); Study on Forecast and Prediction Methods for Tunnel Wall Rock Deformation. Technology of Highway and Transport, 4: 88-92.

[4] Jin, X.G., Li, X.H., Gao, P. and Kang, H.M. (2002); Application of Grey Majorized Model in Tunnel Surrounding Rock Displacement Forecasting. Journal of Chongqing University(Natural Sciecne Edition), 25(1): 1-5.

[5] Zhou, R.Z. and Qiu, G.X. (2001); Displacement Back-Analysis for Deep Foundation Pit Based On BP Neural Networks. China Civil Engineering Journal, 34(6): 60-62.

[6] Li, Y.S., Li, X.P., Zhang, C.L. (2006); The Displacement Prediction of Surrounding Rock Based on BP Neural Network for the Tunnel. Journal of Highway and Transportation Research and Development, 25(supp. 1): 2970-2973.

[7] Chen, Q.N., Zhang, Y.X. and Chen, J.G. (2004); The Displacement Prediction of Surrounding Rock Based on BP Neural Network for the Tunnel. Journal of Highway and Transportation Research and Development, 21(2): 65-69.

[8] Cao, L.J. and Tay, F.E.H. (2003); Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting, IEEE Transactions on Neural Networks, 14(6): 1506-1518.

DOI: 10.1109/tnn.2003.820556

[9] Vapnik, V.N. (1999); An Overview Of Statistical Learning Theory, IEEE Transactions on Neural Networks, 10(5): 988-999.

DOI: 10.1109/72.788640

[10] Sing B. and Goel R.K. (1999); Rock mass classification: a practical approach in Civil Engineering. Elsevier Science Ltd. U. K.

[11] Dong, B., Cao, C. and Lee, S.E. (2005); Applying Support Vector Machines to Predict Building Energy Consumption in Tropical Region, Energy and Buildings, 37(5): 545-553.

DOI: 10.1016/j.enbuild.2004.09.009

[12] Yu B, Yang ZZ and Yao BZ (2006) Bus arrival time prediction using support vector machines. Journal of Intelligent Transportation Systems 10(4): 151-158.

DOI: 10.1080/15472450600981009

[13] Moller, M.F. (1993); A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning, Neural Networks, 6(4): 523-533.

DOI: 10.1016/s0893-6080(05)80056-5