浅埋隧道开挖岩体移动分析的模糊测度方法

发布时间：2018-01-15 16:37

本文关键词：浅埋隧道开挖岩体移动分析的模糊测度方法　出处：《河北大学》2014年硕士论文　论文类型：学位论文

【摘要】：本文针对山区浅埋隧道开挖引起的岩体移动问题，采用模糊测度模型进行预测分析，并以MATLAB为平台进行数值计算。本研究以韩家庄隧道工程为例，确定不同的地质剖面进行岩体移动变形预测分析。计算中对理论模型中所涉及的参数，采用人工神经网络方法进行确定。利用模糊神经网络方法分析结果表明：（1）山区浅埋隧道开挖后地表位移变形呈现出明显的非对称性；（2）隧道埋深与地表最大沉降成反比，且埋深越大，，对地表最大沉降值的影响越不敏感；（3）地表坡度的变化对最大沉降值影响较小；（4）隧道跨度的大小与地表最大沉降成正比，且跨度越大，对最大沉降值的影响越敏感。针对韩家庄隧道工程，在利用模糊测度模型进行了具体分析的同时，采用有限单元法对隧道开挖岩体移动进行了数值模拟分析，并将所得数值模拟结果与模糊测度模型计算结果及实测数据进行了对比，使不同方法所获结果能够相互验证。对比结果表明，模糊测度模型计算结果与韩家庄隧道实测结果基本一致，从而为山区浅埋隧道开挖引起的地表下沉预测提供了一种新的方法—模糊神经网络方法。理论分析结果表明，ANSYS软件预测所获的最大下沉值与实测最大下沉值二者基本一致，但整体下沉分布与实测值相差较大，主要是边界效应不符合工程实际，而模糊测度模型则可以解决这一问题。
[Abstract]:In this paper, fuzzy measure model is used to predict and analyze rock mass movement caused by shallow tunnel excavation in mountainous area, and numerical calculation is carried out on the platform of MATLAB. In this study, Hanjiazhuang tunnel project is taken as an example. The parameters involved in the theoretical model are determined by artificial neural network method. The results of fuzzy neural network analysis show that the surface displacement and deformation of shallow buried tunnel in mountainous area show obvious asymmetry after excavation. (2) the depth of the tunnel is inversely proportional to the maximum settlement of the earth's surface, and the greater the depth, the less sensitive it is to the maximum settlement of the surface; (3) the change of surface slope has little effect on the maximum settlement value; (4) the size of tunnel span is proportional to the maximum surface settlement, and the larger the span is, the more sensitive it is to the maximum settlement value. In view of Hanjiazhuang tunnel project, the fuzzy measure model is used to carry out the concrete analysis, and the finite element method is used to simulate the rock mass movement of the tunnel excavation. The numerical simulation results are compared with the calculated results of fuzzy measure model and the measured data, so that the results obtained by different methods can be verified mutually. The comparison results show that the calculation results of fuzzy measure model are basically consistent with the measured results of Hanjiazhuang tunnel. It provides a new method for prediction of surface subsidence caused by shallow tunnel excavation in mountainous area, which is called fuzzy neural network method. The theoretical analysis results show that this method can be used to predict the surface subsidence of shallow buried tunnels. The maximum subsidence value predicted by ANSYS software is basically consistent with the measured maximum subsidence value, but the difference between the overall subsidence distribution and the measured value is quite large, mainly because the boundary effect is not in line with the engineering practice. Fuzzy measure model can solve this problem.
【学位授予单位】：河北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U456.3

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