Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stre...Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stress on the intact rock strength are investigated and compared with laboratory results from the literature. To normalize differences in laboratory testing conditions, the stress state is used as the objective parameter in the artificial neural network model predictions. The variations of major principal stress of rock material with intermediate principal stress, minor principal stress and stress state are investigated. The artificial neural network simulations show that for the rock types examined, none were independent of intermediate principal stress effects. In addition, the results of the artificial neural network models, in general agreement with observations made by others, show （a） a general trend of strength increasing and reaching a peak at some intermediate stress state factor, followed by a decline in strength for most rock types; （b） a post-peak strength behavior dependent on the minor principal stress, with respect to rock type; （c） sensitivity to the stress state, and to the interaction between the stress state and uniaxial compressive strength of the test data by the artificial neural networks models （two-way analysis of variance; 95% confidence interval）. Artificial neural network modeling, a self-learning approach to polyaxial stress simulation, can thus complement the commonly observed difficult task of conducting true triaxial laboratory tests, and/or other methods that attempt to improve two-dimensional （2D） failure criteria by incorporating intermediate principal stress effects.展开更多
This paper introduced a performance evaluating approach of computer communication system based on the simulation and measurement technology, and discussed its evaluating models. The result of our experiment showed tha...This paper introduced a performance evaluating approach of computer communication system based on the simulation and measurement technology, and discussed its evaluating models. The result of our experiment showed that the outcome of practical measurement on Ether-LAN fitted in well with the theoretical analysis. The approach we presented can be used to define various kinds of artificially simulated load models conveiently, build all kinds of network application environments in a flexible way, and exert sufficiently the widely-used and high-precision features of the traditional simulation technology and the reality, reliability, adaptability features of measurement technology.展开更多
文摘Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stress on the intact rock strength are investigated and compared with laboratory results from the literature. To normalize differences in laboratory testing conditions, the stress state is used as the objective parameter in the artificial neural network model predictions. The variations of major principal stress of rock material with intermediate principal stress, minor principal stress and stress state are investigated. The artificial neural network simulations show that for the rock types examined, none were independent of intermediate principal stress effects. In addition, the results of the artificial neural network models, in general agreement with observations made by others, show （a） a general trend of strength increasing and reaching a peak at some intermediate stress state factor, followed by a decline in strength for most rock types; （b） a post-peak strength behavior dependent on the minor principal stress, with respect to rock type; （c） sensitivity to the stress state, and to the interaction between the stress state and uniaxial compressive strength of the test data by the artificial neural networks models （two-way analysis of variance; 95% confidence interval）. Artificial neural network modeling, a self-learning approach to polyaxial stress simulation, can thus complement the commonly observed difficult task of conducting true triaxial laboratory tests, and/or other methods that attempt to improve two-dimensional （2D） failure criteria by incorporating intermediate principal stress effects.
基金Supported by the National 863 High-Tech Project of China!(863-30 0-02-09-99) by Key Research Project ofHubei Province!(99
文摘This paper introduced a performance evaluating approach of computer communication system based on the simulation and measurement technology, and discussed its evaluating models. The result of our experiment showed that the outcome of practical measurement on Ether-LAN fitted in well with the theoretical analysis. The approach we presented can be used to define various kinds of artificially simulated load models conveiently, build all kinds of network application environments in a flexible way, and exert sufficiently the widely-used and high-precision features of the traditional simulation technology and the reality, reliability, adaptability features of measurement technology.
