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基于? -SVR算法的隧道围岩位移演化规律预测
引用本文:陈秋南,张永兴,赵明华,刘新荣.基于? -SVR算法的隧道围岩位移演化规律预测[J].岩土力学,2006,27(4):591-593.
作者姓名:陈秋南  张永兴  赵明华  刘新荣
作者单位:1. 湖南科技大学,土木工程学院,湖南,湘潭,411201;湖南大学,土木工程学院,长沙,410082
2. 重庆大学,土木工程学院,重庆,400045
3. 湖南大学,土木工程学院,长沙,410082
基金项目:湖南省交通科技创新项目;湖南省教育厅科研项目;中国科学院资助项目
摘    要:针对目前广泛应用的灰色理论、遗传算法(GA)和人工神经网络(ANN)等方法预测隧道稳定性的缺陷,提出应用稳健性能较好的? -SVR(support vector machine)算法对非对称连拱隧道围岩位移演化规律进行预测研究。应用加速混合遗传算法搜索? -SVR最优参数,以提高? -SVR的预测能力。将预测结果与灰色理论、BP神经网络预测结果进行比较,显示? -SVR算法学习和预测精度高。

关 键 词:隧道工程  ?  -SVR算法  围岩位移  演化规律
文章编号:1000-7598(2006)04-0591-03
收稿时间:2004-08-04
修稿时间:2004-11-11

Forecasting evolution of tunnel surrounding rock displacement by ε-SVR
CHEN Qiu-nan,ZHANG Yong-xing,ZHAO Ming-hua,LIU Xin-rong.Forecasting evolution of tunnel surrounding rock displacement by ε-SVR[J].Rock and Soil Mechanics,2006,27(4):591-593.
Authors:CHEN Qiu-nan  ZHANG Yong-xing  ZHAO Ming-hua  LIU Xin-rong
Institution:1. School of Civil Engineering, Hunan University of Science & Technology, Xiangtan 411201, China; 2. College of Civil Engineering, Chongqing University, Chongqing 400045, China; 3. College of Civil Engineering, Hunan University, Changsha 410082, China
Abstract:Because the prediction accuracy of gray theory, GA and ANN algorithm is insufficiency for tunnel rock surrounding stability, the method of ?-support vector machines was applied to researching of evolution law for tunnel rock surrounding displacement; and in order to enhance the learning efficiency of ?- support vector machines and the capability of forecasting, the accelerated hybrid genetic algorithm (GA) is used for optimizing parameters of ?-support vector machines. Comparison the forecasting results of gray theory, GA and ANN and monitoring results for tunnel rock surrounding displacement, the results show the learning efficiency and prediction accuracy of ?-support vector machines is superior to gray theory, GA and ANN obviously.
Keywords:tunnel engineering  ?-support vector machines  surrounding rock displacement  evolution law  
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