Parameter identification of unsaturated seepage model of core rockfill dams using principal component analysis and multi-objective optimization

Abstract

The seepage behavior of core walls is an important aspect of safety monitoring of core rockfill dams. The permeability coefficient of unsaturated soil of the core wall is a nonlinear function not a constant. Since there are many seepage monitoring points in the core wall, parameter identification of unsaturated seepage model of soil core wall based on the data of these points is a high-dimensional multi-objective optimization problem. In this paper, a parameter identification method of unsaturated seepage in the core wall is proposed, in which an unsaturated seepage model, principal component analysis (PCA), and elitist non-dominated sorting genetic algorithm (NSGA-II) are combined. The seepage monitoring sequence of measuring points in saturated and unsaturated regions of the core wall is synthesized to a few comprehensive variables by PCA respectively. The NSGA- II is adopted to optimize the objective functions established by these comprehensive variables to obtain the Pareto-optimal solutions. The efficiency, accuracy, and robustness to the error of the proposed method have been confirmed in a fictitious dam and an actual dam. The predicted seepage pressure with the Brooks-Corey (BC) model is in best agreement with the measured data, and the simulated seepage field is more in line with the actual situation.