Abstract
A new method integrating support vector machine (SVM), particle swarm optimization (PSO) and chaotic mapping (CPSO-SVM) was proposed to predict the deformation of tunnel surrounding rock mass. Since chaotic mapping was featured by certainty, ergodicity and stochastic property, it was employed to improve the convergence rate and resulting precision of PSO. The chaotic PSO was adopted in the optimization of the appropriate SVM parameters, such as kernel function and training parameters, improving substantially the generalization ability of SVM. And finally, the integrating method was applied to predict the convergence deformation of the Xiakeng tunnel in China. The results indicate that the proposed method can describe the relationship of deformation time series well and is proved to be more efficient.
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Foundation item: Project(NCET-08-0662) supported by Program for New Century Excellent Talents in University of China; Project(2010CB732006) supported by the Special Funds for the National Basic Research Program of China; Projects(51178187, 41072224) supported by the National Natural Science Foundation of China
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Li, Sj., Zhao, Hb. & Ru, Zl. Deformation prediction of tunnel surrounding rock mass using CPSO-SVM model. J. Cent. South Univ. 19, 3311–3319 (2012). https://doi.org/10.1007/s11771-012-1409-3
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DOI: https://doi.org/10.1007/s11771-012-1409-3