Study on Viscoelastic Deformation Monitoring Index of an RCC Gravity Dam in an Alpine Region Using Orthogonal Test Design

Huang, Yaoying; Wan, Zhiyong

doi:10.1155/2018/8743505

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…In order to obtain the elastic modulus of the dam body and the deformation modulus of the dam foundation during the operation period, in this section, we implemented a hybrid method combining orthogonal experimental design, a neural network, and a numerical simulation to solve the mathematical model of the optimized inversion of the elastic modulus of the reinforced arch dam. [33][34] F I G U R E 7 Curves of measured values, fitted values and the water pressure component of the radial horizontal displacement at each measurement point…”

Section: Optimized Inverse Analysis Of Elastic Modulusmentioning

confidence: 99%

Evaluation of the mechanical parameters of a reinforced concrete dam based on multi‐source data

Huang

Xie

et al. 2022

Structural Concrete

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In this research, we studied a roller‐compacted concrete (RCC) arch dam which was reinforced during the construction period to investigate the properties of mechanical parameters of the dam concrete after long‐term operation. Firstly, multiple methods involving core drilling, computerized tomography (CT), spectral analysis of the surface wave (SASW) of elastic waves, and an on‐site inspection were adopted to detect the quality of the dam concrete, which was followed by a forward‐analysis of the measured displacement of the arch dam and the water pressure component of the measured displacement was separated through regression analysis of statistical model of radial horizontal displacement. Subsequently, an optimization inversion of the elastic modulus of the dam body and dam foundation was conducted using a hybrid optimization method. The results showed that the compressive strength of the concrete in the body of the reinforced arch dam was over 30 MPa and the inversion results for the elastic modulus of the dam body were in the range of 30.1 ~ 33.3 GPa. According to the research results of multi‐source inspected and monitoring data during the operation period, the results suggest that the performance of the concrete of the dam body is in accordance with design requirements and the reinforced arch dam is properly operated.

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Section: Optimized Inverse Analysis Of Elastic Modulusmentioning

confidence: 99%

Evaluation of the mechanical parameters of a reinforced concrete dam based on multi‐source data

Huang

Xie

et al. 2022

Structural Concrete

Self Cite

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“…Fig. 1 Burgers rheology model small probability method, confidence interval method, limit state method, structural analysis method and other methods [18,49,50]. In actual engineering, when using the abovementioned methods to formulate the monitoring indexes for rockfill dams and perform numerical simulations, the physical and mechanical parameters of the rockfill materials are mostly treated as fixed values.…”

Section: Monitoring Hybrid Index Determinationmentioning

confidence: 99%

A hybrid monitoring model of rockfill dams considering the spatial variability of rockfill materials and a method for determining the monitoring indexes

Ran

Yang

Zhang³

et al. 2022

J Civil Struct Health Monit

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To further enhance the prediction accuracy of the monitoring model and improve the rationality of the method for determining the monitoring indexes, this paper considers the impact of the spatial variability of rockfill materials on the displacement of the dam. Based on the stochastic finite element method (SFEM), a new rockfill dam displacement monitoring hybrid model is established, and a method to determine the hybrid index for rockfill dam displacement monitoring is proposed by multiindex amalgamation. First, the SFEM is used to simulate the spatial variability of the rockfill mechanical parameters in the process of calculating the water pressure component and the time-dependent component. On this basis, the expression of each component is fitted to construct a stochastic finite element method hybrid model (SFEMH model) of the rockfill dam. Subsequently, by predicting the displacement of the dam and separating each displacement component, the displacement monitoring hybrid index of the rockfill dam is determined by combining the confidence interval method and the most unfavourable components. Finally, the SFEMH model and hybrid index are applied to analyse the actual monitoring data of a rockfill dam. The results show that the proposed model and the method for determining the displacement monitoring hybrid index are scientific and reasonable. The prediction accuracy and effect of the proposed novel model outperform those of other methods in terms of many evaluation indicators, and the reliability of the monitoring indexes is significantly improved. The proposed hybrid model and hybrid index provide a new method for the efficient operation management and accurate safety performance evaluation of rockfill dams.

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“…Thus, hybrid models are developed. [25][26][27] The hydrostatic component of dam deformation is established using FEM and is commonly expressed with a polynomial of reservoir level. The coefficients of the polynomial are obtained by regression analysis of the computed dam deformations corresponding to different reservoir levels.…”

Section: Introductionmentioning

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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Study on Viscoelastic Deformation Monitoring Index of an RCC Gravity Dam in an Alpine Region Using Orthogonal Test Design

Cited by 9 publications

References 10 publications

Evaluation of the mechanical parameters of a reinforced concrete dam based on multi‐source data

Evaluation of the mechanical parameters of a reinforced concrete dam based on multi‐source data

A hybrid monitoring model of rockfill dams considering the spatial variability of rockfill materials and a method for determining the monitoring indexes

Separate modeling technique for deformation monitoring of concrete dams

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