隧道各向异性软岩力学参数反演及蠕变研究
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摘要
在高地应力条件下具有层理构造的横观各向同性软岩中开挖隧道后,软弱围岩会发生显著的流变变形,直接影响隧道围岩的稳定性及支护结构的长期服役性能。采用数值模型模拟木寨岭隧道大战沟斜井试验洞的开挖蠕变过程,建立并验证了隧道宏观变形特征值与围岩力学计算参数之间的BP神经网络。在此基础上根据6+90试验洞现场变形监测数据,反演得到了该处炭质板岩的塑性及蠕变力学参数。应用另两处监测断面的现场监测曲线验证了基于BP神经网络的反演方法及所得炭质板岩力学参数的可靠性。另外分析了横观各向同性炭质板岩的开挖蠕变力学变形性质,对高地应力条件下横观各向同性软弱围岩中隧洞开挖及支护结构的设计、施工具有重要的指导意义。
Under high geo-stress condition, significant creep deformation of transversely isotropic soft surrounding rock with bedding structure occurs after tunnel excavation,which directly affect the stability of tunnel surrounding rock mass and long-term service performance of the supporting structure. The numerical model of test cave in Dazhangou inclined shaft of Muzhailing railway tunnel was used to simulate the cave excavation and subsequent creep of surrounding rock mass. A BP neural network between the macroscopic deformation values of the feature points on surrounding rock mass free surface and mechanical parameters of surrounding rock mass for calculation was developed and validated. On this basis,the plastic and creep mechanical parameters of the carbonaceous slate were obtained based on the back analysis of one-year on-site monitoring deformation data of monitoring section 1 in 6+ 90 test cave. The inversion parameters were applied in the numerical simulation to calculate the deformation curve with time of two monitoring sections in 6+ 90 test cave and 6+ 80 shear test cave,respectively. The numerical simulation result could reflect the creep deformation characteristics of transversely isotropic carbonaceous slate surrounding rock mass under high geo-stress condition. Both the reliability of inversion method based on BP neural network and the resulting mechanical parameters of transversely isotropic carbonaceous slate were proved. Furthermore,the mechanical and deformation properties of transversely isotropic carbonaceous slate during excavation and creep were analyzed,which helps to predict the long-term deformation of tunnel surrounding rock mass and has guiding significance for the design and construction of tunnel excavation and supporting structure in transversely isotropic soft rock mass under high stress condition.
Under high geo-stress condition, significant creep deformation of transversely isotropic soft surrounding rock with bedding structure occurs after tunnel excavation,which directly affect the stability of tunnel surrounding rock mass and long-term service performance of the supporting structure. The numerical model of test cave in Dazhangou inclined shaft of Muzhailing railway tunnel was used to simulate the cave excavation and subsequent creep of surrounding rock mass. A BP neural network between the macroscopic deformation values of the feature points on surrounding rock mass free surface and mechanical parameters of surrounding rock mass for calculation was developed and validated. On this basis,the plastic and creep mechanical parameters of the carbonaceous slate were obtained based on the back analysis of one-year on-site monitoring deformation data of monitoring section 1 in 6+ 90 test cave. The inversion parameters were applied in the numerical simulation to calculate the deformation curve with time of two monitoring sections in 6+ 90 test cave and 6+ 80 shear test cave,respectively. The numerical simulation result could reflect the creep deformation characteristics of transversely isotropic carbonaceous slate surrounding rock mass under high geo-stress condition. Both the reliability of inversion method based on BP neural network and the resulting mechanical parameters of transversely isotropic carbonaceous slate were proved. Furthermore,the mechanical and deformation properties of transversely isotropic carbonaceous slate during excavation and creep were analyzed,which helps to predict the long-term deformation of tunnel surrounding rock mass and has guiding significance for the design and construction of tunnel excavation and supporting structure in transversely isotropic soft rock mass under high stress condition.
引文
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[17]文辉辉,尹健民,秦志光,等.BP神经网络在隧道围岩力学参数反演中的应用[J].长江科学院院报,2013,30(2):47-51,56.
[18]文辉辉.隧道围岩力学参数反分析研究[D].武汉:长江科学院,2012.
[19]周学良.隧道围岩变形监测与围岩参数的反演分析[D].合肥:合肥工业大学,2007.
[20]刘高,张帆宇,李新召,等.木寨岭隧道大变形特征及机理分析[J].岩石力学与工程学报,2005,24(增2):5521-5526.
[21]Shalabi F I.FE analysis of time-dependent behavior of tunneling in squeezing ground using two different creep models[J].Tunnelling and Underground Space Technology,2005,20(3):271-279.
[22]杨蒙,谭跃虎,李二兵,等.基于敏感性分析的围岩力学参数反演方法研究[J].地下空间与工程学报,2014,10(5):1030-1038.
[2]Li B,Jiang Y J,Mizokami T,et al.Anisotropic shear behavior of closely jointed rock masses[J].International Journal of Rock Mechanics and Mining Sciences,2014,71:258-271.
[3]吕擎峰,韩文峰.金川岩体各向异性与巷道支护变形破坏关系探讨[J].岩石力学与工程学报,2000,19(2):149-152.
[4]李晓红,夏彬伟,李丹,等.深埋隧道层状围岩变形特征分析[J].岩土力学,2010,31(4):1163-1167.
[5]Choi M J,Yang H S.Anisotropic analysis of tunnel in transversely isotropic rock[J].Tunnel and Underground Space,2005,15(6):391-399.
[6]Klopcic J,Logar J.Effect of relative orientation of anisotropy planes to tunnel axis on the magnitude of tunnelling displacements[J].International Journal of Rock Mechanics and Mining Sciences,2014,71:235-248.
[7]Zhang Z Z,Zhang J H,Hou D Q,et al.Displacement analytic solution of a deep elliptical tunnel in transversely isotropic rock mass[A]//Advances in Metallurgical and Mining Engineering[C].StafaZurich:Trans Tech Publications Ltd,2012:593-597.
[8]Wang S Y,Sloan S W,Tang C A,et al.Numerical simulation of the failure mechanism of circular tunnels in transversely isotropic rock masses[J].Tunnelling and Underground Space Technology,2012,32(11):231-244.
[9]张成平,韩凯航,张顶立,等.城市软弱围岩隧道塌方特征及演化规律试验研究[J].岩石力学与工程学报,2014,33(12):2433-2442.
[10]陈志敏.高地应力软岩隧道围岩压力研究和围岩与支护结构相互作用机制分析[D].兰州:兰州交通大学,2012.
[11]李鹏飞,田四明,赵勇,等.高地应力软弱围岩隧道初期支护受力特性的现场监测研究[J].岩石力学与工程学报,2013,32(增2):3509-3519.
[12]Fan Q Y,Lu J Y,Zhu Z.Experimental study on creep of surrounding rock mass in argillaceous soft rock tunnel[J].Applied Mechanics and Materials,2012,193-194:826-830.
[13]王更峰,张永兴,熊晓晖,等.深埋大变形隧道炭质板岩蠕变特性试验[J].公路交通科技,2012,29(9):95-102.
[14]Wang L G,Li L,Liu X.Creep Analysis of Effect on Long-term Stability of Tunnel Roof Rock[A]//2010International Conference on Mine Hazards Prevention and Control[C].Paris:Atlantis Press,2010:42-45.
[15]高玮,冯夏庭,郑颖人.地下工程围岩参数反演的仿生算法及其工程应用研究[J].岩石力学与工程学报,2002,21(增2):2521-2526.
[16]董呈龙.基于监控量测和数值模拟的隧道围岩参数反演分析[D].武汉:武汉理工大学,2009.
[17]文辉辉,尹健民,秦志光,等.BP神经网络在隧道围岩力学参数反演中的应用[J].长江科学院院报,2013,30(2):47-51,56.
[18]文辉辉.隧道围岩力学参数反分析研究[D].武汉:长江科学院,2012.
[19]周学良.隧道围岩变形监测与围岩参数的反演分析[D].合肥:合肥工业大学,2007.
[20]刘高,张帆宇,李新召,等.木寨岭隧道大变形特征及机理分析[J].岩石力学与工程学报,2005,24(增2):5521-5526.
[21]Shalabi F I.FE analysis of time-dependent behavior of tunneling in squeezing ground using two different creep models[J].Tunnelling and Underground Space Technology,2005,20(3):271-279.
[22]杨蒙,谭跃虎,李二兵,等.基于敏感性分析的围岩力学参数反演方法研究[J].地下空间与工程学报,2014,10(5):1030-1038.