Back Analysis of the Permeability Coefficient of a High Core Rockfill Dam Based on a RBF Neural Network Optimized Using the PSO Algorithm

• Published in: Mathematical problems in engineering Vol. 2015; no. 2015; pp. 1 - 15
• Main Authors: Chi, Shichun, Liu, Zhenping, Ni, Shasha
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015; John Wiley & Sons, Inc
• Subjects: Algorithms; Approximation; Civil engineering; Coefficients; Dams; Engineering; Finite element method; Genetic algorithms; Groundwater; Hydraulics; Mathematical models; Methods; Neural networks; Neurons; Optimization; Osmometers; Osmosis; Parameter estimation; Parameters; Particle swarm optimization; Permeability; Radial basis function; Rockfill; Rockfill dams; Seepage; Swarm intelligence; Technology assessment; Water levels; China
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2015/124042

It is important to determine the seepage field parameters of a high core rockfill dam using the seepage data obtained during operation. For the Nuozhadu high core rockfill dam, a back analysis model is proposed using the radial basis function neural network optimized using a particle swarm optimization algorithm (PSO-RBFNN) and the technology of finite element analysis for solving the saturated-unsaturated seepage field. The recorded osmotic pressure curves of osmometers, which are distributed in the maximum cross section, are applied to this back analysis. The permeability coefficients of the dam materials are retrieved using the measured seepage pressure values while the steady state seepage condition exists; that is, the water lever remains unchanged. Meanwhile, the parameters are tested using the unstable saturated-unsaturated seepage field while the water level rises. The results show that the permeability coefficients are reasonable and can be used to study the real behavior of a seepage field of a high core rockfill dam during its operation period.