The application of the finite layer & triangular prism element method to the 3D ground subsidence and stress analysis caused by mining is presented. The layer elements and the triangular prism elements have been a...The application of the finite layer & triangular prism element method to the 3D ground subsidence and stress analysis caused by mining is presented. The layer elements and the triangular prism elements have been alternatively used in the numerical simulation system, the displacement pattern, strain matrix, elastic matrix, stiffness matrix, load matrix and the stress matrix of the layer element and triangular prism element have been presented. By means of the Fortran90 programming language, a numerical simulation system based on finite layer & triangular prism element have been built up, and this system is suitable for subsidence prediction and stress analysis of all mining condition and mining methods. Comparing with the infinite element method, this approach dramatically reduces the size of the set of equations that need to be solved, and greatly reduces the amount of data preparation required. It not only saves the internal storage, and the computation time, but also decreases the cost.展开更多
文摘The application of the finite layer & triangular prism element method to the 3D ground subsidence and stress analysis caused by mining is presented. The layer elements and the triangular prism elements have been alternatively used in the numerical simulation system, the displacement pattern, strain matrix, elastic matrix, stiffness matrix, load matrix and the stress matrix of the layer element and triangular prism element have been presented. By means of the Fortran90 programming language, a numerical simulation system based on finite layer & triangular prism element have been built up, and this system is suitable for subsidence prediction and stress analysis of all mining condition and mining methods. Comparing with the infinite element method, this approach dramatically reduces the size of the set of equations that need to be solved, and greatly reduces the amount of data preparation required. It not only saves the internal storage, and the computation time, but also decreases the cost.
