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作 者:赵杰[1] 刘历胜 王桂萱[1] 孙晓艳[1] ZHAO Jie LIU Lisheng WANG Guixuan SUN Xiaoyan(Research and Development Center of Civil Engineering Technology, Dalian University, Dalian 116622, China)
机构地区:[1]大连大学土木工程技术研究与开发中心,辽宁大连116622
出 处:《防灾减灾工程学报》2016年第4期640-645,651,共7页Journal of Disaster Prevention and Mitigation Engineering
基 金:辽宁省教育厅项目(2012-0102517);大连市科学技术基金项目(2010J21DW013)资助
摘 要:详细收集了大连地铁东港广场至港湾广场区间的资料,制定了合理的监测方案,通过对监测数据的处理,合理预测了围岩的变形趋势及特点,为二次衬砌的支护提供了合理时机。通过对量测结果的分析,确定了围岩的反演参数;对反演参数弹性模量E和水平侧压力系数K进行了水平划分,利用正交试验设计和均匀试验设计方法,以选取的断面为参考,利用FLAC3D数值模拟和BP神经网络相结合的方法,分别建立了神经网络的训练样本和检测样本,从而得到了大连地铁暗挖区间的两个围岩参数:弹性模量E和水平侧压力系数K;然后通过反演得到的围岩参数,利用FLAC3D对暗挖隧道进行正演分析,将变形结果与监测结果进行比较,均符合规范要求。Based on detailed information collected in Dalian Metro,in this paper,a reasonable monitoring program is developed,through which the deformation trend and characteristics of surrounding rock could be reasonably predicted.As a result,a reasonable time for secondary lining support is identified.Through the analysis of measurement results,the inversion parameter of surrounding rock is determined.The inversion parameters,i.e.,the elastic modulus Eand horizontal lateral pressure coefficient K,are horizontal differentiated.Based on the orthogonal experiment design and uniform experimental design method,for the selected reference section,FLAC^(3D) numerical simulation and the method of combining the BP neural network are adopted to establish the neural network training samples and monitoring samples,respectively.Then dalian underground subway interval of two parameters of surrounding rock,elastic modulus Eand horizontal lateral pressure coefficient K,are calculated.Then inversion parameters were applied to the forward analysis of the underground tunnel by FLAC^(3D).The analyzed and monitored deformations are compared and indicated that the results conform to the requirements of the specification.
关 键 词:地铁隧道 监控量测 位移反分析 FLAC3D BP神经网络
分 类 号:U452.1[建筑科学—桥梁与隧道工程]
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