Entity

Time filter

Source Type


Zhang G.,Huazhong University of Science and Technology | Liu M.,Highway Administration of Qianjiang City | Liu A.,Highway Administration of Qianjiang City | Xu H.,Huazhong University of Science and Technology
Yingyong Lixue Xuebao/Chinese Journal of Applied Mechanics | Year: 2010

Study shows that there is normalization characteristic about the curves of stress and plastic-strain of middle-dense sands in the range of routine stress under the stress path of constant p and increasing p. The triaxial test data are normalized by choosing proper normalization parameters. The neural networks are trained by regarding the normalized data as samples and then the double-yield-surfaces model of sands described by neural networks are obtained, which show that the double yield surfaces under the two stress paths are different. The modeling method suggested in this paper reflects the stress-path-dependency. Source


Zhang G.-Y.,Huazhong University of Science and Technology | Liu A.-G.,Highway Administration of Qianjiang City | Liu M.-R.,Highway Administration of Qianjiang City | Xu H.,Huazhong University of Science and Technology
Wuhan Ligong Daxue Xuebao/Journal of Wuhan University of Technology | Year: 2010

To reduce the cost of numerical modeling, and to improve the accuracy of double-yield-surfaces model, a modeling method to use the normalization characteristic of the curves of stress and plastic-strain of middle-dense sands under the stress path of constant p is present. The triaxial test data are normalized by choosing proper normalization parameters. The neural networks are trained by regarding the normalized data as samples and then the double-yield-surfaces model of sands described by neural networks is obtained. The emulation value of the neural networks agrees well, which shows that proposed modeling method is reasonable. It can achieve probabilistic optimization automatically based on all test data by using the modeling method, and can reduce the interference of noise signal, and lower the influence caused by test data. Source

Discover hidden collaborations