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为研究隧道膨胀性围岩蠕变参数及其蠕变特性,结合龙泉山1号隧道围岩变形监测资料和相关试验,利用BP神经网络联合有限差分软件FLAC3D的方法,选取Burgers粘弹性模型与Mohr-Coulomb屈服准则串联形成的修正Burgers模型为膨胀性围岩的蠕变本构模型,以隧道典型断面的拱顶沉降监测数据为基础信息,反演岩体的蠕变参数。利用所得参数进行正演计算,结果表明,用于反演和未用于反演断面的拱顶沉降计算值与监测数据在量值上相当,变形趋势也基本相同。因此,文中所述方法可以获得较为合理的膨胀性围岩蠕变参数,并较好地弥补室内试验的不足,进而为今后类似工程提供理论依据及指导。
In order to study the creep parameters and creep characteristics of expansive surrounding rock in tunnel, combined with the deformation monitoring data and relevant tests of surrounding rock of Longquanshan No.1 tunnel, the BP neural network combined with finite difference software FLAC3D was used to select the Burgers viscoelastic model and Mohr The modified Burgers model formed by the series of Coulomb yield criterion is the creep constitutive model of expansive surrounding rock. The creep parameters of the rock mass are retrieved based on the dome settlement monitoring data of typical sections of the tunnel. The results of forward modeling using the obtained parameters show that the calculated values and monitoring data of vault settlement used for inversion and not used for inversion are similar in magnitude and deformation trends are basically the same. Therefore, the method described in this paper can obtain more reasonable creep parameters of expansive surrounding rock, and make up for the deficiency of indoor experiment well, so as to provide theoretical basis and guidance for similar projects in the future.