The dynamic finite element model calibration method of concrete dams based on strong-motion records and multivariate relevant vector machines

Cheng, Lin; Zheng, Dongjian; Tong, Fei; Zheng, Sixun

doi:10.21595/jve.2016.16953

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…The calibration of the FEA is a process of tuning and improving the FEA computation, which aims to predict and analyze the future behavior of the object better. Efficient methods regarding FEA calibration have been reported in many cases [ 5 , 6 , 7 ]. Abrahamsson and Kammer applied the experimental frequency response to calibrate the dynamic FEA model [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…The results indicate that the application of the frequency response offers a scientific and reliable calibration deviation metric. Different calibration methods combining dynamic experimentation have been presented, including the Levenberg–Marquardt minimizer [ 6 ] and the genetic algorithm [ 7 ] to optimize parameters. Additional calibration studies of complex structures were carried out based on the fundamental principles above.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Bao

Chen

et al. 2020

Sensors

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The demand for efficient and accurate finite element analysis (FEA) is becoming more prevalent with the increase in advanced calibration technologies and sensor-based monitoring methods. The current research explores a deep learning-based methodology to calibrate FEA results. The utilization of monitoring reference results from measurements, e.g., terrestrial laser scanning, can help to capture the actual features in the static loading process. We learn the deviation sequence results between the standard FEA computations with the simplified geometry and refined reference values by the long short-term memory method. The complex changing principles in different deviations are trained and captured effectively in the training process of deep learning. Hence, we generate the FEA sequence results corresponding to next adjacent loading steps. The final FEA computations are calibrated by the threshold control. The calibration reduces the mean square errors of the FEA future sequence results significantly. This strengthens the calibration depth. Consequently, the calibration of FEA computations with deep learning can play a helpful role in the prediction and monitoring problems regarding the future structural behaviors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Bao

Chen

et al. 2020

Sensors

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Finite Element Model Calibration of Sandwich Structure Based on Mixed Numerical Experimental Technique

Ručevskis

Wesołowski

Kovaļovs

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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This paper presents the implementation of the mixed numerical experimental technique for the finite element (FE) model calibration of a sandwich structure by using modal data. Model calibration is conducted by minimizing the difference between the numerical and experimental dynamic parameters. In this study, instead of the direct minimisation of the response discrepancy, the experiment design and the response surface method is employed to solve the inverse (calibration) problem. Numerical eigenfrequencies are obtained by performing FE calculations in sample points derived by Latin Hypercube experiment design where each sample point represents a unique engineering constant configuration in the FE model of the sandwich panel. Using the information on the dynamic responses of the panel in the sample points, response functions describing the relationship between the engineering constants and the calculated eigenfrequencies are obtained by means of response surface method. Genetic algorithm is employed to solve the minimisation of the response discrepancy where the response functions instead of FE calculations are used to obtain the numerical modal frequencies. The verification results show that proposed method is capable to calibrate a numerical model with a good prediction accuracy and small uncertainties.

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Verification, validation, and uncertainty quantification (VVUQ) in structural analysis of concrete dams

Salamon,

Hariri-Ardebili

2024

Front. Built Environ.

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Over the past three decades, advancements in computational power and numerical methods have significantly enhanced the role of structural analyses in the design and safety assessment of dams. Simulating concrete dam behavior, particularly in interactions with reservoir water and rock foundations, poses formidable computational challenges. Additionally, the need to define uncertainties related to material parameters, loading conditions, and modeling strategy adds complexity to the modeling process, therefore, quantifying sources of uncertainty is crucial for maintaining credibility and confidence in analysis results. This paper provides a synthesis and an overview of existing research and presents a generic framework for evaluating the credibility of advanced structural analysis methods for concrete dams, with a focus on their limitations and associated uncertainties. The methodology includes a comprehensive process for structural analysis, verification, validation, and uncertainty quantification, aiming to facilitate condition assessments of concrete dams.

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The dynamic finite element model calibration method of concrete dams based on strong-motion records and multivariate relevant vector machines

Cited by 4 publications

References 22 publications

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Finite Element Model Calibration of Sandwich Structure Based on Mixed Numerical Experimental Technique

Verification, validation, and uncertainty quantification (VVUQ) in structural analysis of concrete dams

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