Structural Safety Monitoring of High Arch Dam Using Improved ABC-BP Model

Zhu, Yantao; Gu, Chongshi; Zhao, Erfeng; Song, Jianshe; Guo, Zhiyun

doi:10.1155/2016/6858697

Cited by 15 publications

(9 citation statements)

References 11 publications

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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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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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“…The corresponding mathematical models are denoted by

{\overset{falsê}{u}}_{H} ((), H, x, y, z)

{\overset{falsê}{u}}_{S} ((), T, x, y, z)

, and

{\overset{falsê}{u}}_{T} ((), θ, x, y, z)

, respectively. Traditionally,

{\overset{falsê}{u}}_{H} ((), H, x, y, z)

is constructed as 21–23

{\overset{falsê}{u}}_{H} ((), H, x, y, z) = \sum_{k = 1}^{N} \sum_{l, m, n = 0}^{3} A_{klmn} {(H - H_{0})}^{k} x^{l} y^{m} z^{n} = \sum_{k = 1}^{N} \sum_{l, m, n = 0}^{3} A_{klmn} {∆ H}^{k} x^{l} y^{m} z^{n}

where H denotes the water level on a certain date, H 0 represents the water level on the start date, the relative water level ∆H is calculated as H − H 0 , N , k , l , m , and n are parameters, and A klmn is a coefficient.…”

Section: Introductionmentioning

confidence: 99%

“…For gravity dams and arch dams, the N values are 3 and 4, respectively.

{\overset{falsê}{u}}_{S} ((), T, x, y, z)

is given as 21–23

{\overset{falsê}{u}}_{S} ((), T, x, y, z) = \sum_{j, k = 0}^{1} \sum_{normall, m, n = 0}^{3} B_{jklmn} italicsin \frac{2 italicπjT}{365} italiccos \frac{2 italicπkT}{365} x^{l} y^{m} z^{n}

where T denotes the number of days between a certain date and the start date, j , k , l , m , and n are parameters, and B jklmn is a coefficient.…”

Section: Introductionmentioning

confidence: 99%

“…

{\overset{falsê}{u}}_{S} ((), T, x, y, z) = \sum_{j, k = 0}^{1} \sum_{normall, m, n = 0}^{3} B_{jklmn} italicsin \frac{2 italicπjT}{365} italiccos \frac{2 italicπkT}{365} x^{l} y^{m} z^{n}

where T denotes the number of days between a certain date and the start date, j , k , l , m , and n are parameters, and B jklmn is a coefficient.

{\overset{falsê}{u}}_{T} ((), θ, x, y, z)

is established as 21–23

{\overset{falsê}{u}}_{T} ((), θ, x, y, z) = \sum_{j, k = 0}^{1} \sum_{l, m, n = 0}^{3} C_{jklmn} θ^{j} {(\ln θ)}^{k} x^{l} y^{m} z^{n},

where θ is defined as

\frac{T}{100}

, j , k , l , m , and n are parameters, and C jklmn is a coefficient.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid hydraulic‐seasonal‐time model for predicting the deformation behaviour of high concrete dams during the operational period

Yang

Chen

et al. 2021

Struct Control Health Monit

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Summary Considering the limitations of the traditional hydraulic‐seasonal‐time (HST) model, this study proposes a hybrid modeling method for the deformation prediction of high concrete dams during the operational period. First, the elastic finite element (FE) method is applied to simulate the interactive effects of structural properties, topography, geology, and high hydrostatic load on the deformation behaviour of high concrete dams in operating conditions. The hybrid model of hydrostatic pressure deformation is established. The hybrid hydraulic‐seasonal‐time (HHST) model is proposed. Second, the self‐adaptive stochastic inertia weight, dynamic learning factors, and velocity and position parameters are introduced to improve the particle swarm optimization (PSO) algorithm. The hybrid prediction approach is developed through the comprehensive application of the HHST model and the improved PSO algorithm. The proposed methodology is adopted for the Jinping I project, which is the highest concrete arch dam in the world. The analysis results indicate that the model accuracy is good and that the model performance is promoted.

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“…A large number of engineering experiences demonstrate that an abnormal deformation is often the direct indication on dam deterioration 3–5 . According to the monitoring data, statistical models, 6–8 deterministic models 9 and hybrid models 10–13 are often established to predict the structural behavior, which plays a vital role in the quantitative evaluation and the feedback control on the dam safety. However, in the conventional models, it is quite demanding to select the influence factors due to the probably non‐linear correlations 14 .…”

Section: Introductionmentioning

confidence: 99%

Risk probabilistic assessment of ultrahigh arch dams through regression panel modeling on deformation behavior

Zhao

2021

Struct Control Health Monit

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Summary The deformations of ultrahigh arch dams can comprehensively indicate the dynamic variations of their structural behavior to judge the normal or not for timely discovering anomalies. First, the panel data features on the deformation behavior are extracted to effectively indicate the overall structural evolution of the Jinping I arch dam combing with the time series and the cross‐section series. Afterwards, a regression panel model (RPM) on the multi‐dimensional variables is proposed to model the deformation panel data consisted of multi‐monitoring points synchronously. Subsequently, an innovative functional relationship between the measured values and the real‐time risk probabilistic function is established due to the RPM estimation accuracy. In order to estimate the risk probability of the whole arch dam, the Copula function is used to build a multivariate joint probability distribution function to indicate the correlation among the random variables. The proposed methods are validated by an application on the Jinping I arch dam to evaluate its risk probability, which explores a novel approach for the arch dam safety assessment.

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Structural Safety Monitoring of High Arch Dam Using Improved ABC-BP Model

Cited by 15 publications

References 11 publications

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

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

Hybrid hydraulic‐seasonal‐time model for predicting the deformation behaviour of high concrete dams during the operational period

Risk probabilistic assessment of ultrahigh arch dams through regression panel modeling on deformation behavior

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