摘要
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 characteristics and obtain the creep parameters of swelling rock in tunnel,the method that combines BP neural network with FLAC3 D which is based on finite difference method is adopted,combined with the deformation monitoring data and related experiments in No. 1 tunnel of Longquan Mountain. The modified Burgers model is selected as the creep model of the swelling rock,in which the Burgers visco-elastic model is in series with Mohr-Coulomb yield criterion. The creep parameters inversion is proceeded based on the monitoring data of typical tunnel section. Numerical simulation is conducted with the creep parameters acquired from inversion and results indicate that the calculated crown settlement is similar to the monitoring data and the deformation tendency is also the same at sections which are used or not used in inversion. Therefore,the reasonable creep parameters of swelling rock can be reached with the proposed method,making up for the deficiency of the laboratory test at the same time. And then,the research could be the theoretical foundation of similar projects in the future.
出处
《地下空间与工程学报》
CSCD
北大核心
2016年第6期1504-1510,共7页
Chinese Journal of Underground Space and Engineering
基金
重庆市科技攻关项目(CSTC2011AC6212)
重庆市应用开发项目(CSTC2013YYKFA30003)
关键词
膨胀岩
蠕变特性
参数反演
BP神经网络
隧道
swelling rock
creep characteristics
parameter inversion
BP neural network
tunnel
