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

Yang, Guang; Gu, Hao; Chen, Xudong; Zhao, Kupeng; Qiao, Dong; Xing-quan, Chen

doi:10.1002/stc.2685

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…The temperature and aging components are still formulated using statistical models as described in the Statistical Models section. The hydrostatic component is established using FEM with designated mechanical parameters of dam concretes and foundation rocks (e.g., elastic modulus) and is formulated with a polynomial of reservoir level as follows 40 where h is reservoir level and the coefficients a i (i = 1, 2, 3) are obtained by regression analysis of the computed dam deformations corresponding to different reservoir levels.…”

Section: Methods For Constructing Deformation Monitoring Models Of Co...mentioning

confidence: 99%

Separate modeling technique for deformation monitoring of concrete dams

Yin

2022

Structural Health Monitoring

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Deformation monitoring is an important aspect of safety control for concrete dams. Deformation monitoring models (such as statistical models and hybrid models) are extensively applied to predict concrete dam deformation and derive confidence interval of normal deformation for anomaly detection. Deformation monitoring models for concrete dams mainly consist of hydrostatic component, temperature component, and aging component. The optimum parameters of individual components are simultaneously determined by least square method for monitoring data fitting. Thus, significant over-fitting of deformation monitoring models may be induced by mutual compensation among the parameters of model components. In this paper, the Separate Modeling Technique (SMT) is proposed for mitigating the over-fitting problem of deformation monitoring models for concrete dams. Firstly, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the aging component of displacement sequence. Proper mathematical formulation of the aging component can be established, and the problem of improperly presupposing the mathematical form of aging component in the process of constructing traditional deformation monitoring models is well addressed. In this study, the hydrostatic component is represented by the Hybrid Response Surface (HRS), which is formulated using numerical simulation with varying water levels and material parameters. The displacement variation caused by water level fluctuation is identified in terms of isothermal conditions and is used to calibrate the material parameters in the HRS. The temperature component is separated through subtracting the hydrostatic and aging components from displacement time series and then is expressed with proper mathematical formulations. Finally, hybrid models for displacement monitoring of concrete dams are established by combining the separately formulated components. The Separate modeling technique is applied to formulate crest displacement of the YL concrete gravity dam. The false alarm rate of displacement monitoring and a new model selection criterion (namely over-fitting coefficient) are adopted to compare various deformation monitoring models. It is shown that the over-fitting levels of deformation monitoring models can be effectively reduced using the SMT. The deformation monitoring models constructed with the SMT are of better accuracy in displacement prediction and present no false alarm of displacement monitoring for the tested period.

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Section: Methods For Constructing Deformation Monitoring Models Of Co...mentioning

confidence: 99%

Separate modeling technique for deformation monitoring of concrete dams

Yin

2022

Structural Health Monitoring

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“…In the dam safety evaluation, the analysis of the measured data of stress and strain is indispensable. At present, many researchers have studied the deformation of the high arch dam combined with the measured data, [2][3][4] but the stress of a high arch dam is less studied. The stress calculation of a high arch dam can be studied using the measured data of strain gauge groups.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis method for the importance of stress influencing factors of a high arch dam during the operation period using SPA–OSC–PLS

Han

Yao

et al. 2022

Structural Contr & Hlth

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Stress analysis is an important part of the health monitoring and evaluation of a high arch dam. At present, a reasonable quantitative analysis method for the importance of stress influencing factors of a high arch dam during the operation period remains lacking. According to measured data of strain gauge groups, a quantitative analysis method is proposed integrating succession projection algorithm (SPA), orthogonal signal correction (OSC), and partial least squares (PLS). First, based on the stress calculation of the strain gauge group of a concrete dam, the statistical model of high arch dam stress is established.Second, independent variables are preprocessed using SPA and OSC to screen out effective influencing factors without noise signals. Finally, the principal component with the strongest explanation for the dependent variable is extracted using PLS, and the importance of stress influencing factors of a high arch dam during the operation period is quantitatively separated. Results show that the SPA-OSC-PLS method can effectively eliminate the effects of multiple correlations, information overlap, and noise among influencing factors. In addition, the predictive ability and interpretability of the SPA-OSC-PLS method are superior to the PLS and SPA-PLS methods, which can reasonably obtain the importance of stress influencing factors of a high arch dam. The proposed method can accurately reveal the change law and action mechanism of the stress of a high arch dam during the operation period.

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“…6,7 Furthermore, many scholars have conducted extensive research and provided useful discussions on the prediction of dam deformation and seepage. 8–13…”

Section: Introductionmentioning

confidence: 99%

“…6,7 Furthermore, many scholars have conducted extensive research and provided useful discussions on the prediction of dam deformation and seepage. [8][9][10][11][12][13] At present, three types of conventional monitoring models are used for hydraulic structure monitoring: statistical models, deterministic models, and hybrid models. 8 Statistical models based on multiple linear regression (MLR) and its advanced forms, such as stepwise multiple regression, robust regression, or ridge regression, have provided remarkable prediction results.…”

Section: Introductionmentioning

confidence: 99%

Structural deformation prediction model based on extreme learning machine algorithm and particle swarm optimization

Jiang

Zhao

2022

Structural Health Monitoring

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In this paper, an extreme learning machine (ELM) algorithm based on particle swarm optimization (PSO) is proposed to predict structural deformation. Taking an aqueduct located in Tiantai County, Zhejiang, China, as a case study, a series of observations of the aqueduct vertical displacements and crack openings were used to train a neural network. Then, variables representing environmental factors (air temperature), hydraulic factors (water level), and aging were selected as the influence factors input into the prediction model. Finally, the proposed PSO–ELM model was used to predict the vertical deformation and crack opening of the aqueduct, and the predicted results were compared with the monitored values using four evaluation indexes: mean absolute error ( MAE), mean squared error ( MSE), maximum absolute error ( S), and correlation coefficient ( R). The prediction results obtained using the PSO–ELM model were then compared with those obtained using the evolutionary ELM, conventional ELM, back propagation neural network, long short-term memory, and multiple linear regression models. The results indicate that the proposed PSO–ELM model has an evidently superior predictive ability, with higher values of R and lower values of MAE, MSE, and S. The proposed model can therefore be confidently used to serve as a tool similar to a “weather forecast” function to predict the vertical deformation and crack openings of an aqueduct and may be employed for other structural monitoring applications as well.

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

Cited by 13 publications

References 33 publications

Separate modeling technique for deformation monitoring of concrete dams

Separate modeling technique for deformation monitoring of concrete dams

Quantitative analysis method for the importance of stress influencing factors of a high arch dam during the operation period using SPA–OSC–PLS

Structural deformation prediction model based on extreme learning machine algorithm and particle swarm optimization

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