Uncertainty Updating of Finite Element Models Using Interval Analysis

Shan, Dayong; Chai, Y. H.; Dong, Hao; Li, Zhonghui

doi:10.1142/s0219455420410126

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…It means that the decomposed IMFs are not only significant nonstationary but also nonlinearity. It is known from earthquake engineering that the input signal of the ground motions has inevitable nonlinear and nonstationary properties, and structural seismic responses are also nonlinear and nonstationary [40]. That is, this observation aligns with the visual interpretation of RPs.…”

Section: Cable-stayed Shaking Table Model Bridgesupporting

confidence: 70%

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Nonlinear and nonstationary detection and quantification of multi-scale measured signals for bridge structure

Shan,

Yu,

Long

et al. 2024

Meas. Sci. Technol.

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The assessment of nonlinear and nonstationary levels in measured bridge signals is a vital step in system identification and long-term health monitoring for the bridge structure. The field-measured signals from the bridge structure are inherently weak and multiscale, so a specific adaptive variational mode decomposition (AVMD) is proposed to decompose them and extract their included multi-scale features. Combination the adaptability of Empirical Mode Decomposition (EMD) with the dimensionality reduction of Principal Component Analysis (PCA), the number of inherent mode functions (IMFs) that need to be given in the conventional variational mode decomposition(VMD) is adaptively determined in the proposed AVMD. The original measured signals from the bridge structure multiscale are subsequently decomposed by AVMD into the multiscale IMFs with the lowest cross-correlation. Then, the recurrence plot (RP) and recurrence quantification analysis (RQA) are introduced into the detection and quantification of the measured signals, and the nonlinear and nonstationary quantification indexes are constructed to describe quantitatively the nonlinear and nonstationary levels. The stabilities and accuracies of three nonlinear and three nonstationary quantification indexes are comparatively discussed by the nonlinear and nonstationary detection and quantification of three well-defined simulated signals. The Shannon entropy (ENTR) and Trapping time(TT) indexes are subsequently determined to quantify the nonlinear and nonstationary levels of the measured signals, respectively. Finally, the proposed algorithm and quantification indexes are applied to the nonlinear and nonstationary detection and quantification of the measured signals from the real-world bridge structures. It is shown from the validation and discussion that the proposed algorithm is available to detect and quantify the nonlinear and nonstationary levels of the measured multiscale signal from the real-world bridge structure.

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Section: Cable-stayed Shaking Table Model Bridgesupporting

confidence: 70%

“…The structural responses of the main girder and pylons are measured by the unidirectional piezoelectric accelerometers. More details about the shaking table experiment are presented in the literature[40]. The accelerometer layout on the main girder for the cable-stayed bridge model is shown in figure12.…”

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confidence: 99%

Nonlinear and nonstationary detection and quantification of multi-scale measured signals for bridge structure

Shan,

Yu,

Long

et al. 2024

Meas. Sci. Technol.

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Field monitoring and analysis for cumulative longitudinal movements of girder ends for the medium-span suspension bridges

Deng²,

Dong

et al. 2022

J Civil Struct Health Monit

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An Interval Model Updating Method Based on Meta-Model and Response Surface Reconstruction

Zhan

Qin

Zhang

et al. 2022

Int. J. Str. Stab. Dyn.

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In this paper, a new interval finite element model updating method is proposed for interval identification of structural parameters based on meta-model and response surface reconstruction. The lower and upper bounds of the uncertain structural parameters are determined by solving the optimization problem which minimizes the difference between the interval of the predicted and measured responses. The response surface models are reconstructed based on the resampling technique for mapping the relationship between a single input and a single output. Then the accurate interval of the responses during the iteration step can be efficiently estimated using the vertex method. Meanwhile, the Gaussian process regression model (GPRM) is constructed as the meta-model to replace the finite element model for calculating the responses of the system to improve computational efficiency. Several numerical and experimental examples are investigated to elucidate the feasibility of the proposed method in the interval identification of structural parameters. Obtained outcomes have demonstrated that the proposed method outperforms many existing approaches in the literature, especially for the nonlinear monotonously non-increasing problem.

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Uncertainty Updating of Finite Element Models Using Interval Analysis

Cited by 3 publications

References 21 publications

Nonlinear and nonstationary detection and quantification of multi-scale measured signals for bridge structure

Nonlinear and nonstationary detection and quantification of multi-scale measured signals for bridge structure

Field monitoring and analysis for cumulative longitudinal movements of girder ends for the medium-span suspension bridges

An Interval Model Updating Method Based on Meta-Model and Response Surface Reconstruction

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