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Mathematical Problems in Engineering
Volume 2015, Article ID 124042, 15 pages
Research Article

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

1Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
2College of Civil and Architecture Engineering, Heilongjiang Institute of Technology, Harbin 150050, China

Received 15 June 2015; Revised 13 October 2015; Accepted 15 October 2015

Academic Editor: Antonino Laudani

Copyright © 2015 Shichun Chi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.