考虑空间效应的围岩位移释放系数的确定

发布时间：2018-03-05 09:42

本文选题：隧道　切入点：空间约束效应　出处：《重庆大学》2014年硕士论文　论文类型：学位论文

【摘要】：隧道是赋存于岩土体中的地下结构物,它和地上结构物的不同之处在于隧道周围的岩土体是它的主要研究对象，岩土体的稳定性和变形规律是隧道工程人员非常关心的一个问题。由于受到赋存环境的强烈影响，所以岩土体的物理、力学、构造和时间性质都非常复杂，因此科学的认识它的各种性质以及隧道开挖后它和支护结构之间的相互作用是一个比较困难的问题，但是国内外的科研工作者和隧道工程设计人员通过大量的理论分析和工程实践，已经取得了重大的成果，建立了比较完善的隧道工程设计理论。隧道开挖面的存在对附近的围岩产生了一个径向约束的作用，相当于施加了一个虚拟支护力，围岩的位移和应力会随着开挖面的推进而发生变化,我们称之为开挖面的空间效应。本文将在隧道空间效应理论已取得的研究成果的基础上，对以下几个问题进一步研究,论文的主要工作如下: ①对空间效应的产生机理、反映空间效应约束损失的约束损失因子进行分析；并且在考虑了空间效应的基础上分析围岩的位移释放，建立了位移释放系数和约束损失因子之间的关系；最后对考虑空间效应的围岩-支护作用机制进行分析。 ②采用有限元分析软件对浅埋直墙拱形隧道的循环开挖进行数值模拟,得出围岩的纵断面变形曲线和位移释放系数曲线,分析隧道的空间效应。由于在开挖面空间效应的影响范围内,围岩主要产生弹塑性变形，因此本文还将基于摩尔-库伦强度理论，采用数值模拟的方法分析岩体的物理力学参数以及隧道的埋深比（H/R）对围岩位移释放系数的影响。 ③在②中研究成果基础上，确定围岩位移释放系数的影响参数，以IV级围岩为例，采用有限元数值模拟的方法建立样本集。以影响参数的取值作为输入量，，位移释放系数的取值作为输出量，训练BP人工神经网络；并且对其进行验证，确保网络训练的精度。 ④以重庆两江桥渝中连接隧道某一区段为例,采用③中得到的神经网络预测出围岩的位移释放系数曲线,并且就位移释放系数在选择支护时机时的应用进行分析。在实际工程中，合理的预估围岩的位移释放系数对于指导隧道安全合理的施工具有重要的意义。传统的方法主要通过理论计算或数值模拟来确定围岩的位移释放系数，而这些方法往往比较复杂而且需要耗费较多的时间。本文提出采用人工神经网络方法实现围岩位移释放系数的预测；与传统方法相比，其简单有效并且具有较高的精度，从而具有极高的工程应用价值和实际意义。
[Abstract]:Tunnel is an underground structure existing in rock and soil. The difference between tunnel and above ground structure is that the rock and soil around the tunnel is its main research object. The stability and deformation of rock and soil are a problem of great concern to tunnel engineers. Due to the strong influence of the environment, the physical, mechanical, structural and temporal properties of rock and soil are very complicated. Therefore, it is a difficult problem to understand scientifically its various properties and the interaction between it and the supporting structure after the excavation of the tunnel. However, through a large number of theoretical analysis and engineering practice, researchers and tunnel designers at home and abroad have made great achievements and established a relatively perfect theory of tunnel engineering design. The existence of tunnel excavation surface has a radial constraint effect on the surrounding rock nearby, which is equivalent to applying a virtual support force, and the displacement and stress of surrounding rock will change with the advance of excavating surface. We call it the spatial effect of excavated surface. Based on the research results of tunnel spatial effect theory, the following problems are further studied in this paper. The main work of this paper is as follows:. The main contents are as follows: (1) the mechanism of spatial effect is analyzed, and the constraint loss factor reflecting the constraint loss of spatial effect is analyzed, and the displacement release of surrounding rock is analyzed based on the consideration of spatial effect. The relationship between displacement release coefficient and constraint loss factor is established. Finally, the mechanism of surrounding rock support with space effect is analyzed. (2) numerical simulation of circular excavation of shallow buried vertical wall arch tunnel is carried out by using finite element analysis software, and the deformation curve and displacement release coefficient curve of surrounding rock are obtained. The spatial effect of tunnel is analyzed. Because the surrounding rock mainly produces elastoplastic deformation in the range of spatial effect of excavating surface, this paper will also base on the Moorl-Coulomb strength theory. The influence of the physical and mechanical parameters of rock mass and the ratio of buried depth of tunnel to the displacement release coefficient of surrounding rock is analyzed by numerical simulation. 3 on the basis of 2 research results, the influence parameters of displacement release coefficient of surrounding rock are determined. Taking class IV surrounding rock as an example, the sample set is established by means of finite element numerical simulation, and the value of influence parameter is taken as the input quantity. The displacement release coefficient is used as the output value to train BP artificial neural network and verify it to ensure the accuracy of network training. 4 taking a section of Yuzhong tunnel connecting Liangjiang Bridge in Chongqing as an example, the displacement release coefficient curve of surrounding rock is predicted by using the neural network obtained in 3, and the application of displacement release coefficient in selecting supporting time is analyzed. In the actual engineering, it is important to estimate the displacement release coefficient of surrounding rock reasonably for guiding the safe and reasonable construction of tunnel. The traditional method mainly determines the displacement release coefficient of surrounding rock by theoretical calculation or numerical simulation. However, these methods are often more complicated and need more time. In this paper, artificial neural network method is proposed to predict the displacement release coefficient of surrounding rock. Compared with the traditional method, the method is simple, effective and has higher accuracy. Therefore, it has high engineering application value and practical significance.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U451.2

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