摘要
将解析反演和数值反演相结合,对试验毛洞的断面形状和所处应力场做出适当的简化,利用对应性原理求解出洞室位移的Kelvin粘弹性解,并以此去拟合试验洞不同位置处的实测位移而得到多组流变参数;分别将其作为FLAC3D数值模型蠕变模式的输入参数,得出各自对应的实际断面形状和实际应力场条件下试验洞的蠕变位移数值解。然后,利用BP神经网络,对各组流变参数和其对应的试验洞同一位置处的蠕变位移数值解进行训练,建立起两者之间的非线性关系;利用训练好的网络,依据实测蠕变位移值得出了围岩流变参数;最后,利用隧道实测位移数据对反演的流变参数进行了数值正分析验证。
Based on the correspondence principles,the analytical visco-elastic solution of round tunnel displacement under hydrostatic pressure is given using Kelvin constitutive law.After simplifying the testing tunnel as a round one subjected to hydrostatic pressure,the preliminary rheological parameters are obtained by using the analytical solution to fit the monitoring displacement data.A FLAC3D model is then established in which the testing tunnel is of the real shape and is under the real ground stress.The preliminary rhelogical parameters are then adopted as parameters required under the creep option of FLAC3D,and the numerical solution of the test tunnel displacement is then given.With the help of BP neural network,the mapping relationship between the rheological parameters and the corresponding numerical solution of displacement are established.And the rheological parameters are then back analyzed by applying monitoring data to this trained network.Finally,the verification of the usefulness and reliability of the back-analyzed rheological parameters is given by implementing a normal calculation process to a tunnel.
出处
《探矿工程（岩土钻掘工程）》
2012年第2期74-79,共6页
Exploration Engineering:Rock & Soil Drilling and Tunneling
关键词
隧道
围岩
流变
粘弹性位移
反分析
数值模拟
tunnel
surrounding rock
rheology
visco-elastic displacement
back analysis
numerical simulation
