Analysis Of Embankment Dam Seepage Problem And Prototype Observation

Seepage problem is one of the most important factors of the safety of engineering, so the research of seepage problem has significant theoretical meaning and application value. This thesis discusses seepage problem from finite element method for solving unconfined seepage field, permeability coefficient inversion and seepage monitoring model. The high-accuracy computing method which reflects the seepage law and the permeability coefficients which reflect the medium seepage characteristic correctly are premises to solve seepage field accurately, and they depend on each other. The seepage monitoring model describes seepage law of a special structure. The main work of this thesis as follows:1. The free surface, as the boundary of seepage field, is to be solved for unconfined seepage problem, so the analysis of the problems is nonlinear, and the settlement of free surface need iterative computation. In this thesis, a FEM program is compiled based on variable permeability coefficient matrix method by APDL, second development tool of general FEM software ANSYS, and the reliability and practicability is demonstrated by an experiment and a case.2. The veracity of result of seepage analysis is relied on the simulation of computing model and accuracy of computing parameters, and the permeability coefficient of medium is one of the most basal and important parameters in seepage analysis. The problem of seepage coefficient inversion is a typical nonlinear problem, but there is no good method to solve the problem. Artificial neural network has very strong ability to solve nonlinear problem, and this thesis just based on the character, applied BP neural network to permeability coefficient inversion.3. In a seepage monitoring model built by ordinary multiple linear regression, the multicollinearity between each monitoring indicator will influence the parameter estimation, enlarge the model error and damage the robustness of model. To avoid multicollinearity's disturbance, partial least-squares regression which can identify system information and noise is introduced to model, and a novel seepage monitoring model is introduced.