Study on MPGA‐BP of Gravity Dam Deformation Prediction

Wang, Xiaoyu; Yang, Kun; Chang-song, Shen

doi:10.1155/2017/2586107

Cited by 15 publications

(13 citation statements)

References 16 publications

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“…First, according to the deformation prototype observation data, statistical models are established with common methods, for example, stepwise regression [30] and back propagation (BP) neural network. [31] Elastic moduli of dam body and foundation are inverted with the finite element analysis software ANSYS, accordingly, hybrid models are established. Second, modified hybrid forecast model is proposed by determining the weight coefficients of each hybrid models with SFLA, which has the function of local optimization and global optimization with efficient computational performance and excellent search capability.…”

Section: Introductionmentioning

confidence: 99%

Modified hybrid forecast model considering chaotic residual errors for dam deformation

Wei

Yuan

et al. 2018

Struct Control Health Monit

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Summary Dam deformation is double effected by the internal and external environments, showing high nonlinear characteristics. Information mining of dam prototype observation data and effective components contained in the residual sequence is limited in conventional dam deformation forecast models. In order to fully explore the complex nonlinear relationship between dam deformation and environmental factors, modified hybrid forecast model considering chaotic residual errors is proposed on the combination of shuffled frog leapfrog algorithm and chaos theory in this study. Hybrid models are established on the basis of dam deformation prototype observation data, and modified hybrid forecast model is established by determining the optimal weight of each hybrid model with shuffled frog leapfrog algorithm. Considering the chaotic characteristics contained in the residual sequence of modified hybrid forecast model, residual sequence is analyzed and forecasted by chaos theory. By superimposing the residual forecast value with the forecast value of modified hybrid forecast model, modified hybrid forecast model considering chaotic residual errors is established. Example shows that, compared with conventional models, the proposed model is better in fitting precision and convergence speed, and forecast capability is significantly improved by considering the effective components contained in the residual sequence, which presents a new method of the deformation forecast for other hydraulic structures.

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Section: Introductionmentioning

confidence: 99%

Modified hybrid forecast model considering chaotic residual errors for dam deformation

Wei

Yuan

et al. 2018

Struct Control Health Monit

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“…Ching-Yun Kao adopted artificial neural networks to monitor the long-term static deformation data of Fei-Tsui Arch Dam [16]. Zhu [17] and Wang [18] applied backpropagation neural network (BP-NN) to construct dam deformation monitoring model and used intelligent algorithm to optimize the parameters of the network. Kang [19] utilized extreme learning machine (ELM) to predict dam deformation and achieved better prediction performance than that of BP-NN.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis

Chen

Lin

et al. 2018

Mathematical Problems in Engineering

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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.

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“…Forecast of random signal in residual sequence with BP neural network BP neural network 38 refers to the multi-layer feed forward network that uses error BP algorithm and exhibits a powerful computing capability. The network can reflect varieties complex mappings.…”

Section: Construction Of Combination Forecast Model Considering Residmentioning

confidence: 99%

Combination forecast model for concrete dam displacement considering residual correction

Wei

Yuan

et al. 2017

Structural Health Monitoring

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In conventional dam displacement monitoring models, forecast precision is below the standard, the fitting residual sequence contains chaotic components, and information mining of dam prototype observation data is limited. In consideration of the chaotic characteristics of the fitting residual sequence in regression model, the multi-scale wavelet analysis is used to decompose and reconstruct the residual sequence in this study; back propagation neural network and autoregressive integrated moving average model are used to forecast the reconstructed residual sequence by identifying the high-frequency and low-frequency characteristics of signals. By superimposing the residual forecast value with the forecast value of regression model, the combination forecast model for concrete dam displacement considering residual correction is proposed. Examples show that, compared with conventional models, the proposed combination model is better in fitting precision and convergence speed. Forecast capability is significantly improved for dam displacement forecast when effective components contained in residual sequence are considered. A new method of displacement forecast for high slope and other hydraulic structures is presented.

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Study on MPGA‐BP of Gravity Dam Deformation Prediction

Cited by 15 publications

References 16 publications

Modified hybrid forecast model considering chaotic residual errors for dam deformation

Modified hybrid forecast model considering chaotic residual errors for dam deformation

Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis

Combination forecast model for concrete dam displacement considering residual correction

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