The mechanical properties of outwash deposits which are taken as unconsolidated geo-materials with the characteristics of non-uniformity, heterogeneity and multiphase have attracted much attention in engineering. Acco...The mechanical properties of outwash deposits which are taken as unconsolidated geo-materials with the characteristics of non-uniformity, heterogeneity and multiphase have attracted much attention in engineering. According to the results of laboratory direct shear test on the remolded samples, the soil particle parameters of numerical model based on in-situ particle size cumulative curves and 3D granular discrete element method were determined. Then, numerical experiments on different lithology, stone content and gradation composition were conducted. The results show that it is not a flat surface but a shear band that yields in the sample. The curve of particle velocity vs distance from the designed shear surface of test model that is taken as a datum plane in the vertical section of sample shows in "S" shape. The shear disturbance area is about twice the maximum diameter of stone blocks. The greater the stiffness of stone is, the rougher the shear surface is. The shear strength of outwash deposits is largely controlled by lithology and stone content, and the bite force between stone blocks is the root reason of larger friction angle. It is also shown that strain hardening and low shear dilatancy occur under high confining pressure as well as possibility of shear shrinkage. But it is easy to behave shear dilatation and strain softening under low confining pressure. The relationship between particle frictional coefficient and stone content presents an approximately quadratic parabola increase. The strain energy first increases and then drops with the increase of frictional energy. The cohesion increases with soil stiffness increasing but decreases with stone stiffness increasing. Numerical results are consistent with the laboratory test results of remolded samples, which indicate that this method can be a beneficial supplement to determine the parameters of engineering deposit bodies.展开更多
In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial n...In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network(ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling(MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.展开更多
基金Project(2011CB013504) supported by the National Basic Research Program of ChinaProjects(50911130366, 11172090) supported by the National Natural Science Foundation of ChinaProject supported by Central University Basic Research Special Fund, China
文摘The mechanical properties of outwash deposits which are taken as unconsolidated geo-materials with the characteristics of non-uniformity, heterogeneity and multiphase have attracted much attention in engineering. According to the results of laboratory direct shear test on the remolded samples, the soil particle parameters of numerical model based on in-situ particle size cumulative curves and 3D granular discrete element method were determined. Then, numerical experiments on different lithology, stone content and gradation composition were conducted. The results show that it is not a flat surface but a shear band that yields in the sample. The curve of particle velocity vs distance from the designed shear surface of test model that is taken as a datum plane in the vertical section of sample shows in "S" shape. The shear disturbance area is about twice the maximum diameter of stone blocks. The greater the stiffness of stone is, the rougher the shear surface is. The shear strength of outwash deposits is largely controlled by lithology and stone content, and the bite force between stone blocks is the root reason of larger friction angle. It is also shown that strain hardening and low shear dilatancy occur under high confining pressure as well as possibility of shear shrinkage. But it is easy to behave shear dilatation and strain softening under low confining pressure. The relationship between particle frictional coefficient and stone content presents an approximately quadratic parabola increase. The strain energy first increases and then drops with the increase of frictional energy. The cohesion increases with soil stiffness increasing but decreases with stone stiffness increasing. Numerical results are consistent with the laboratory test results of remolded samples, which indicate that this method can be a beneficial supplement to determine the parameters of engineering deposit bodies.
基金Project(2013CB035402) supported by the National Basic Research Program of ChinaProjects(51105048,51209028) supported by the National Natural Science Foundation of China
文摘In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network(ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling(MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.
