Abstract
Effective deformation monitoring is vital for the structural safety of super-high concrete dams. The radial displacement of the dam body is an important index of dam deformation, which is mainly influenced by reservoir water level, temperature effect, and time effect. In general, the safety monitoring models of dams are built on the basis of statistical models. The temperature effect of dam safety monitoring models is interpreted using approximate functions or the temperature values of a few points of measurement. However, this technique confers difficulty in representing the nonlinear features of the temperature effect on super-high concrete dams. In this study, a safety monitoring model of super-high concrete dams is established through the radial basis neural network (RBF-NN) and kernel principal component analysis (KPCA). The RBF-NN with strong nonlinear fitting capacity is utilized as the framework of the model, and KPCA with different kernels is adopted to extract the temperature variables of the dam temperature dataset. The model is applied to a super-high arch dam in China, and results show that the Hybrid-KPCA -RBF-NN model has high fitting and prediction precision and thus has practical application value.
Highlights
More than 90 dams with height greater than 200 m are built worldwide, more than 60% of which are concrete dams [1]
On the basis of the traditional safety monitoring statistical model’s establishment theory, radial basis function neural network (RBF-NN) is combined with other methods, such as kernel principle component analysis (KPCA) and modified artificial fish-swarm algorithm
(1) The kernel principal component analysis (KPCA), with three different kernels optimized by modified artificial fishswarm algorithm (MAFSA), are applied to construct input temperature variables of super-high concrete dam for the purpose of capturing the nonlinear features and minimizing the information loss from temperature dataset
Summary
More than 90 dams with height greater than 200 m are built worldwide (dams in construction are included), more than 60% of which are concrete dams [1]. The operation state of these super-high concrete dams is complicated due to the ambient temperature, water pressure, and concrete mechanical and geomechanical factors. The collapse of these dams may pose a serious threat to the downstream areas. The statistical models are dam safety monitoring models and built on the basis of measured data (water pressure, temperature, and dam effect) and some regression methods, including multivariate linear regression [5, 6], stepwise regression [7, 8], partial least squares regression [9, 10], and kernel function partial least squares regressions [11]. Other than the deformation monitoring, statistical models have been extensively applied in other aspects of dam safety monitoring, such as seepage and crack [12,13,14]
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