摘要
In this paper,we present a new method of intelligent back analysis(IBA)using grey Verhulst model(GVM)to identify geotechnical parameters of rock mass surrounding tunnel,and validate it via a test for a main openings of−600 m level in Coal Mine“6.13”,Democratic People's Republic of Korea.The displacement components used for back analysis are the crown settlement and sidewalls convergence monitored at the end of the openings excavation,and the final closures predicted by GVM.The non-linear relation between displacements and back analysis parameters was obtained by artificial neural network(ANN)and Burger-creep viscoplastic(CVISC)model of FLAC3D.Then,the optimal parameters were determined for rock mass surrounding tunnel by genetic algorithm(GA)with both groups of measured displacements at the end of the final excavation and closures predicted by GVM.The maximum absolute error(MAE)and standard deviation(Std)between calculated displacements by numerical simulation with back analysis parameters and in situ ones were less than 6 and 2 mm,respectively.Therefore,it was found that the proposed method could be successfully applied to determining design parameters and stability for tunnels and underground cavities,as well as mine openings and stopes.
采用基于灰色Verhulst模型(GVM)的智能反分析(IBA)方法确定隧道围岩的岩土参数,并通过对朝鲜“6.13”煤矿−600 m水平主巷道试验验证了该方法的准确性。用于反分析的位移分量为开挖结束时监测到的拱顶沉降和边墙收敛量,以及GVM预测的最终闭合量。利用FLAC3D的人工神经网络(ANN)和Burgers蠕变粘塑性(CVISC)模型,得到了位移与反分析参数之间的非线性关系。然后,利用遗传算法(GA)确定了隧道围岩的最优参数,并利用GVM预测了最终开挖和闭坑时的两组实测位移。采用反分析参数数值模拟的计算位移与原位位移的最大绝对误差(MAE)和标准差(Std)分别小于6 mm和2 mm。结果表明,该方法可用于确定巷道、地下空区、矿山空场和采场的设计参数和稳定性。
基金
Project(32-41)supported by the National Science and Technical Development Foundation of DPR of Korea。
