Deformation prediction of tunnel surrounding rock mass using CPSO-SVM model  

作　　者：李邵军 赵洪波 茹忠亮 

机构地区：[1]State Key Laboratory of Geomechanics and Geotechnical Engineering(Institute of Rock and Soil Mechanics,Chinese Academy of Sciences) [2]School of Civil Engineering,Henan Polytechnic University

出　　处：《Journal of Central South University》2012年第11期3311-3319,共9页中南大学学报（英文版）

基　　金：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

摘　　要：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.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.

关 键 词：deformation prediction TUNNEL chaotic mapping particle swarm optimization support vector machine 

分 类 号：U452.12[建筑科学—桥梁与隧道工程]