Efficient Bayesian FFT method for damage detection using ambient vibration data with consideration of uncertainty

Zhang, Feng‐Liang; Kim, Chul‐Woo; Goi, Yoshinao

doi:10.1002/stc.2659

Cited by 38 publications

(18 citation statements)

References 45 publications

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“…This article proposes a statistical analysis to illustrate the theoretical advantage of measuring rotations rather than deflections in static structural system identification, which is also different from the system identification using dynamic data [50][51][52]. With this aim, the analytical expressions for the target parameters are derived with structural system identification using the constrained observability method.…”

Section: Discussionmentioning

confidence: 99%

Robust Static Structural System Identification Using Rotations

Lei

Galant

et al. 2021

Applied Sciences

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Deflections are commonly measured in the static structural system identification of structures. Comparatively less attention has been paid to the possibility of measuring rotations for structural system identification purposes, despite the many advantages of using inclinometers, such as a high resolution and being reference free. Although some work using rotations can be found in the literature, this paper, for the very first time, proposes a statistical analysis that justifies the theoretical advantage of measuring rotations. The analytical expressions for the target parameters are obtained via static structural system identification using the constrained observability method first. Combined with the inverse distribution theory, the probability density function of the estimations of the target parameters can be obtained. Comparative studies on a simply supported bridge and a frame structure demonstrate the advantage of measuring rotations regarding the unbiasedness and the extent of variation in the estimations. To achieve robust parameter estimations, four strategies to use redundant rotations are proposed and compared. Numerical verifications on a bridge structure and a high-rise building have shown promising results.

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Section: Discussionmentioning

confidence: 99%

Robust Static Structural System Identification Using Rotations

Lei

Galant

et al. 2021

Applied Sciences

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“…[1][2][3] Doebling et al 4 and Sohn et al 5 present a comprehensive review of vibration-based damage detection methods before 1996 and between 1996 and 2001, respectively. Recently, a number of advanced methods based on signal processing techniques [6][7][8] and machine learning [9][10][11] have been proposed. Moreover, researchers have developed various techniques to consider the uncertainties and temperature effects for reliable damage detection.…”

Section: Introductionmentioning

confidence: 99%

Sparse damage detection via the elastic net method using modal data

Hou

Wang

Xia

2021

Structural Health Monitoring

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The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

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“…During this procedure uncertainties stem from the modeling errors and/or measurement noise are also considered. Using this posterior uncertainty information, Bayesian operational modal analysis (BAYOMA) = techniques can also be integrated to two‐stage finite element model updating and/or damage detection procedures 16–20 . Different variants of BAYOMA methods are available in the literature such as Bayesian Spectral Trace Approach (BSTA), 21 extended BFFTA for asynchronous vibration data 22 or Hierarchical Bayesian modal identification 23 .…”

Section: Introductionmentioning

confidence: 99%

Probabilistic investigation of error propagation in frequency domain decomposition‐based operational modal analysis

Hızal

Aktaş

2021

Struct Control Health Monit

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Summary Each operational modal analysis (OMA) technique may produce significant errors during the identification procedure due to the applied methodology, environmental/operational conditions, and instrumentation. Consequently, those errors can adversely affect the quality of identified parameters. In this context, this study aims at providing a comprehensive discussion on the propagation of predictions errors in the frequency domain OMA. To mitigate the prediction errors those considered to be induced by modeling and measurement errors, an extended formulation is presented based on a recently developed Modified Frequency and Spatial Domain Decomposition technique. A comprehensive investigation is presented for the probabilistic modeling of output power spectral density (PSD), considering prediction errors. Numerical and real data applications are conducted to show the effectiveness of the proposed methodology.

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Efficient Bayesian FFT method for damage detection using ambient vibration data with consideration of uncertainty

Cited by 38 publications

References 45 publications

Robust Static Structural System Identification Using Rotations

Robust Static Structural System Identification Using Rotations

Sparse damage detection via the elastic net method using modal data

Probabilistic investigation of error propagation in frequency domain decomposition‐based operational modal analysis

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