Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by th...Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the 13P neural network to predict mountain mining subsidence.展开更多
This article discusses the flyash mechanical properties and analyzes stability of two flyash dams under earthquake by finite element methods. It is studied whether the mixture of flyash and clay can be used as the fil...This article discusses the flyash mechanical properties and analyzes stability of two flyash dams under earthquake by finite element methods. It is studied whether the mixture of flyash and clay can be used as the fill for a dam located in an earthquake region.展开更多
文摘Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the 13P neural network to predict mountain mining subsidence.
文摘This article discusses the flyash mechanical properties and analyzes stability of two flyash dams under earthquake by finite element methods. It is studied whether the mixture of flyash and clay can be used as the fill for a dam located in an earthquake region.
