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本文利用石林隧道软岩大变形段现场监控量测数据,结合有限差分程序FLAC3D与BP神经网络,对石林隧道围岩大变形段流变参数进行反演分析,结果表明:数值反演分析结果与实测位移基本吻合。其中开尔文弹性模量EK为234.8MPa,开尔文粘性系数ηK为62.5MPa·d,麦斯韦尔弹性模量EM为247.3MPa,麦斯韦尔粘性系数ηM为5782.6MPa·d。因此利用软岩隧道围岩拱顶沉降以及周边收敛的位移监测值所进行的位移反分析,是一种准确获取流变参数非常有效且实用的方法。
In this paper, the on-site monitoring and measuring data of the large deformation section of soft rock in Shilin Tunnel are combined with the finite difference program FLAC3D and BP neural network to inversely analyze the rheological parameters of the surrounding rock of Shilin Tunnel. The results show that the numerical inversion results are in good agreement with The measured displacement basically coincide. The Young’s modulus EK is 234.8 MPa, the Kelvin viscosity coefficient ηK is 62.5 MPa · d, the Maxwell elastic modulus EM is 247.3 MPa and the Maxwell viscosity coefficient ηM is 5782.6 MPa · d. Therefore, it is a very effective and practical method to accurately obtain the rheological parameters by using the displacement back analysis of displacement monitoring values of surrounding rock cavern settlement and surrounding convergence of soft rock tunnel.