长江科学院院报 ›› 2013, Vol. 30 ›› Issue (2): 47-51.DOI: 10.3969/j.issn.1001-5485.2013.02.010

• 岩土工程 • 上一篇    下一篇

BP神经网络在隧道围岩力学参数反演中的应用

文辉辉1,尹健民2,秦志光1,谢仁红1   

  1. 1. 中交四航工程研究院有限公司 交通基础工程环保与安全重点实验室, 广州 510230;
    2.长江科学院 水利部岩土力学与工程重点实验室, 武汉 430010
  • 收稿日期:2012-08-18 出版日期:2013-02-01 发布日期:2013-02-06
  • 作者简介:文辉辉(1986-),男,湖北天门人,助理工程师,硕士,主要从事岩土工程研究

Application of BP Neural Network to the Back Analysis of Mechanical Parameters of Tunnel Surrounding Rock

WEN Hui-hui1, YIN Jian-min2, QIN Zhi-guang1, XIE Ren-hong1   

  1. 1. Key Laboratory of Environmental Protection & Safety of Transportation Foundation Engineering, CCCC Fourth Harbor Engineering Institute Co., Ltd., Guangzhou 510230, China; 2. Key Laboratory of Geotechnical Mechanics and Engineering of the MWR, Yangtze River Scientific Research Institute, Wuhan 430010, China
  • Received:2012-08-18 Online:2013-02-01 Published:2013-02-06

摘要: 以谷城至竹溪高速公路珠藏洞隧道施工监测为工程依托,根据现场变形监测数据的指数函数回归方程,对最终变形量进行了预测,并基于其预测值,借助BP神经网络的超强非线性映射能力,对隧道围岩力学参数(变形模量E、黏聚力C、内摩擦角φ)进行反演,以及时掌握开挖围岩类型和材料特性参数,为隧道工程施工和设计提供参数依据,从而达到安全施工和优化设计的目的,以实现隧道的信息化施工与设计。

关键词: 最终变形量, BP神经网络, 隧道围岩, 力学参数, 反演

Abstract: The aim of this research is to ensure the construction safety and optimize the design of tunnels using information technology. With the construction of Zhuzang tunnel of Gucheng-Zhuxi highway as an engineering background, we predicted the final deformation by regression equation of exponential function deduced from the field displacement measurement data. Subsequently, on the basis of the predicted deformation, we carried out back analysis on the mechanical parameters (deformation modulus E, cohesion C, internal friction angle φ) of the tunnel's surrounding rock through BP neural network which has good nonlinear mapping ability. The surrounding rock type and material parameters can be obtained in time to provide parameters for the design and construction of the tunnel. 

Key words: final deformation, BP neural network, tunnel's surrounding rock, mechanical parameters, back analysis

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