Optimized multi-output LSSVR displacement monitoring model for super high arch dams based on dimensionality reduction of measured dam temperature field

Zhu, Mingyuan; Chen, Bin; Gu, Chongshi; Wu, Yan Kai; Chen, Weinan

doi:10.1016/j.engstruct.2022.114686

Cited by 19 publications

(3 citation statements)

References 30 publications

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“…Such a model holds significant value in comprehending the operational state of high-arch dams and ensuring their safe and enduring function. Moreover, it constitutes a crucial component of advancing intelligent water conservancy initiatives as part of the broader "four pres" framework [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

Cao,

Wen,

Feng

et al. 2024

Water

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Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. A novel deep learning-based monitoring model for high-arch dams considering multifactor influences and spatiotemporal data correlations is proposed in this paper. First, the measurement points are clustered to capture the spatial relationship. Successive multivariate mode decomposition is applied to extract the common mode components among the correlated points as spatial influencing factors. Second, the relationship between various factors and deformation components is extracted using factor screening. Finally, a deep learning prediction model is constructed with stacked components to obtain the final prediction. The model is validated based on practical engineering. In nearly one year of high-arch dam deformation prediction, the root mean square error is 0.344 and the R2 is 0.998, showing that the modules within the framework positively contribute to enhancing prediction performance. The prediction results of different measurement points as well as the comparison results with benchmark models show its superiority and generality, providing an advancing and practical approach for engineering structural health monitoring, particularly for high-arch dams.

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Section: Introductionmentioning

confidence: 99%

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

Cao,

Wen,

Feng

et al. 2024

Water

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“…To our knowledge, the dam safety operation is vital in economic proft increase of the project [4,5]. Terefore, the actual structural behaviour of the super-high arch dam exists as a traditional issue [6][7][8]. Deformation is the most intuitive indicator of the dam performance, which is largely afected by temperature load.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of Temperature Deformation Characteristics for Super-High Arch Dams considering Solar Radiation Effects

Shao,

Zheng,

et al. 2023

Structural Control and Health Monitoring

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Considering that the effect of solar radiation on the super-high arch dam temperature field remains poorly studied, the calculation accuracy of dam temperature deformation is unable to be guaranteed accordingly. To address the issue, the solar radiation effect is adequately taken into consideration by proposing a practical calculation method based on the ray-tracing algorithm, the precomputation algorithm, and the ASHRAE clear sky model in this paper. With the aid of the ASHRAE clear sky model, the solar radiation received by the super-high arch dam and reservoir water is calculated. The shading effects are calculated by means of the ray-tracing algorithm, and the precomputation technology is introduced to further enhance the computational efficiency. Finally, to guarantee the authenticity of the calculation results, the dam thermodynamic parameters are inversed by employing the hybrid genetic algorithm. Based on the application in a real-life case, we concluded that around one third of the entire dam radial temperature deformation was attributable to solar radiation during continuous sunny days. The analysis results signify a critical role for taking account of the solar radiation in dam deformation calculation. Furthermore, the practicability and utilization prospect of the proposed method was verified.

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“…4 The hydrostatic pressure component is generally constructed in the form of a fourth power polynomial of the upstream relative water depth. 5,6 The temperature component can be constructed using a linear combination of the trigonometric functions of multiple periodic harmonics, [7][8][9] a linear combination of multiple sections of average air temperature, [10][11][12] or a linear combination of the principal component factors obtained by the principal component analysis of the dam thermometers. 13 The aging component is generally simulated by presetting different functions, which can be constructed in the form of a combination of linear function and logarithmic function, [14][15][16] in the form of a combination of linear function and exponential function, [17][18][19] or in the form of a combination of exponential function and polynomial 20 .…”

Section: Introductionmentioning

confidence: 99%

Construction and selection of deformation monitoring model for high arch dam using separate modeling technique and composite decision criterion

Chen,

2023

Structural Health Monitoring

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Deformation prediction is important to ensure the safe and stable operation of arch dams. Statistical models are extensively applied in arch dam deformation monitoring models, which generally include hydrostatic pressure component, temperature component, and aging (irrecoverable) component. In traditional statistical models, aging component is misset, which will cause unreasonable mutual compensation of each component, resulting in overfitting of the overall model. In this paper, the deformation model based on separate modeling technology is, therefore, proposed mitigating the overfitting problem caused by misspecification of the expression of the aging component in traditional statistical models. Dam deformation components related to different effects are extracted from the deformation monitoring sequence with improved complete ensemble empirical mode decomposition with adaptive noise algorithm and equal water level condition. The correct components of the monitoring model are constructed separately. On the one hand, the fitting accuracy of the model is reflected by the coefficient of determination ( R2); on the other hand, the overfitting degree of the model is quantitatively evaluated by the overfitting coefficient (OC), so that the model with high fitting accuracy and prediction accuracy is determined, that is, the optimal model is selected by using the R2-OC criterion. In this paper, displacement monitoring data from measurement points are used for analysis. The results show that the deformation monitoring model based on the separated modeling technique exhibits higher prediction accuracy and lower false alarm rate. The R2-OC criterion better reflects the degree of overfitting of the monitoring model and the real situation of arch dam monitoring and warning, which improves the accuracy of model selection.

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Optimized multi-output LSSVR displacement monitoring model for super high arch dams based on dimensionality reduction of measured dam temperature field

Cited by 19 publications

References 30 publications

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

Numerical Analysis of Temperature Deformation Characteristics for Super-High Arch Dams considering Solar Radiation Effects

Construction and selection of deformation monitoring model for high arch dam using separate modeling technique and composite decision criterion

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