Deformation prediction of tunnel surrounding rock mass using CPSO-SVM model
来源期刊：中南大学学报(英文版)2012年第11期
论文作者：李邵军 ZHAO Hong-bo(赵洪波) RU Zhong-liang(茹忠亮)
文章页码：3311 - 3319
Key words：deformation prediction; tunnel; chaotic mapping; particle swarm optimization; support vector machine
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.
LI Shao-jun(李邵军)1, ZHAO Hong-bo(赵洪波)2, RU Zhong-liang(茹忠亮)2
(1. State Key Laboratory of Geomechanics and Geotechnical Engineering (Institute of Rock and Soil Mechanics, Chinese Academy of Sciences), Wuhan 430071, China;
2. School of Civil Engineering, Henan Polytechnic University, Jiaozuo 454003, China)
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.
Key words:deformation prediction; tunnel; chaotic mapping; particle swarm optimization; support vector machine
