Abstract
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.
Highlights
The earth-rock dam design is widely adopted locally and abroad because of low investment, locally produced raw materials, and simple construction
The basic idea of searching optimum parameters ci, σi, and wij is as follows: the number of neurons in hidden layer begins at 1; this paper adopts particle swarm optimization (PSO) algorithm as training method to search parameters ci, σi, and wij that minimize the difference between real and predicted matrices of the seepage parameters; the error of the network is checked, and if the error goal is not met, the neuron is added; this procedure is repeated until the error goal is met, or the maximum number of neurons is reached
The problem presented in this study is in essence one of estimating dam permeability coefficients while minimizing the error between the observed and computed hydraulic head values
Summary
The earth-rock dam design is widely adopted locally and abroad because of low investment, locally produced raw materials, and simple construction. The present work proposes the use of RBF neural network (choosing Gaussian function as its activation function) for mapping the complex nonlinear relations between water heads and permeability coefficients. The RBF neural network has a simple structure, succinct training, fast convergence speed, and the ability to represent any complicated nonlinear function relations It creates a radial basis network one neuron at a time. It leads to dimension disaster [25] To avoid this phenomenon, this paper selects an optimizing algorithm to determine the RBF neural network structural parameters, which can achieve a better fitting effect and forecast precision with fewer hidden layer neurons. A back analysis model is proposed with the radial basis function neural network optimized using the particle swarm optimization algorithm (PSO-RBFNN) and the technology of finite elements for solving the saturatedunsaturated seepage field. 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 while operating
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