中文摘要：

以郑州地铁工程为依托,现场监测数据为依据,采用敏感分析和数值模拟相结合的研究方法进行地铁盾构隧道土体力学参数的反演分析。利用MATLAB神经网络工具箱计算平台,计算分析土体变形量与其对应的力学参数之间的非线性关系,构建BP神经网络的训练结构;以地表竖向监测位移为输入样本值,对土体力学参数进行位移反分析。研究结果表明：影响地表变形的主要土体力学参数敏感度依次为内摩擦角（φ）、弹性模量（E）、内聚力（C）、泊松比（μ）。应用FLAC3D软件模拟分析地铁区间隧道盾构施工过程的力学特征,并将其成果作为反演分析的样本数集;杂填土层弹性模量E1为7.60 MPa,内摩擦角φ1为22.5°;粉土层弹性模量E2为19.68 MPa,内摩擦角φ2为27.7°;粉质黏土层弹性模量E3为12.98 MPa,内摩擦角φ3为19.5°。现场应用证明了该方法的有效合理性。

英文摘要：

Taking Zhengzhou subway tunnel project and its work field data as the basis, backward analysis of soil parameters for subway shield ttmnel was conducted by the method of combining sensitivity analysis and numerical simulation. The FLAC3D software was used to simulate and analysis the mechanical properties of subway shield construction process, to got the backward analysis sample set was analyzed. Based on MATLAB neural network toolbox computing platform, the training structure for BP neural network was built to calculate and analyze the nonlinear relationship between the soil deformation and its mechanical parameters. Then taking the measured displacement data as sample input data for the backward analysis model, the soil mechanical parameters can be gained. The results show that the influence degree of the main parameters for deformation of ground surface is in the order of internal friction angle （φ）, elastic modulus （E）, cohesion （C） and poisson ratio （μ）. Elastic modulus of reclamation soil layer is 7.60 MPa and internal friction angle is 22.5°; elastic modulus of silt layer is 19.68 MPa and internal friction angle is 27.7°; Elastic modulus of silt-clay layer is 12.98 MPa and internal friction angle is 19.5°. This method is effective and rational by field applications.