Forecasting evolution of tunnel surrounding rock displacement by ? -SVR

CHEN Qiu-nan; ZHANG Yong-xing; ZHAO Ming-hua; LIU Xin-rong

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Forecasting evolution of tunnel surrounding rock displacement by ? -SVR

更新时间：2026-06-25

- Forecasting evolution of tunnel surrounding rock displacement by ? -SVR
- Vol. 27, Issue 4, (2006)
- 作者机构：
  
  1. 湖南科技大学土木工程学院,湖南,湘潭,411201
  2. 湖南大学土木工程学院,长沙,410082
  3. 重庆大学土木工程学院,重庆,400045
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU45
- Online First：05 November 2013，
  
  Published：2006
- 稿件说明：
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CHEN Qiu-nan, ZHANG Yong-xing, ZHAO Ming-hua, et al. Forecasting evolution of tunnel surrounding rock displacement by ? -SVR[J]. 2006, 27(4).
DOI：

CHEN Qiu-nan, ZHANG Yong-xing, ZHAO Ming-hua, et al. Forecasting evolution of tunnel surrounding rock displacement by ? -SVR[J]. 2006, 27(4). DOI：

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。

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.

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