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FLAC和神经网络在隧道位移反分析中的应用

Application of neural network and FLAC to the back analysis of tunnel displacement

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【作者】 彭剑文赵奎曹宗权马乾天刘明荣

【Author】 PENG Jian-wena,ZHAO Kuib,CAO Zong-quanc,MA Qian-tianc,LIU Ming-rongc(a.School of Architectural and Surveying and Mapping Engineering; b.Tungsten Resource Efficient Development and Application Research Center of the Ministry of Education; c.School of Resource and Environmental Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China)

【机构】 江西理工大学建筑与测绘工程学院江西理工大学钨资源高效开发及应用技术教育部工程研究中心江西理工大学资源与环境工程学院

【摘要】 应用三维有限差分程序FLAC3D和BP神经网络对隧道位移进行分析,使用正交实验和FLAC3D正演结果作为样本,用神经网络建立围岩位移与反演参数的映射关系.反演得出了围岩的弹性模量和初始地应力测压系数,并使用FLAC3D正算验证反演参数的精度.结果表明:可搜索得出反演参数的最优解,实现在隧道围岩中的位移反分析.可将反演结果用于隧道的设计,反演精度满足工程要求.

【Abstract】 This paper studies the application of neural network and FLAC3D to the back-analysis of displacements of tunnel displacement,using the learning and testing samples based on orthogonal test design and FLAC3D numerical simulation.The potential mapping between parameters and surrounding rock displacement was established using neural network.The modulus of elasticity and lateral pressure coefficient of surrounding rock were obtained by verifing the precision of inversion parameter.The results show that it can solve the problem by searching parameters of back-analysis,which can be derived to achieve the displacement back analysis in tunnel displacement.The inversion results can be feedback for the design of tunnel.The results of back-analysis are accord with the accuracy of engineering.

  • 【文献出处】 有色金属科学与工程 ,Nonferrous Metals Science and Engineering , 编辑部邮箱 ,2011年06期
  • 【分类号】U451.2
  • 【被引频次】4
  • 【下载频次】181
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