Modal identification of structures from input/output data using the expectation-maximization algorithm and uncertainty quantification by mean of the bootstrap

Cara, Javier

doi:10.1002/stc.2272

Cited by 12 publications

(6 citation statements)

References 33 publications

(44 reference statements)

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“…It is a trade-off between the detection of small changes and a low false alarm rate. The chosen confidence level of γ = 0.95 is used in many modal parameter uncertainty related applications [13,14,33], but higher confidence levels can be evaluated analogously with the proposed methodology. In such a case, it is possible that the small change of the mode shapes in damage scenario d 1 will not be considered as significant anymore.…”

Section: Applicationmentioning

confidence: 99%

Uncertainty quantification of the Modal Assurance Criterion in operational modal analysis

Greś

Döhler

Mevel

2021

Mechanical Systems and Signal Processing

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The Modal Assurance Criterion (MAC) is a modal indicator designed to decide whether the mode shapes used in its computation are corresponding to the same mode. During structural monitoring, it can be applied to evaluate changes in the mode shapes. When the mode shapes are estimated from measurement data, the MAC inherits their statistical properties, thus is afflicted with statistical uncertainty. The evaluation of this uncertainty is particularly relevant when the MAC estimate is close to 1, where 1 indicates equal mode shapes. In structural monitoring, it can be used to assess changes in mode shapes after early damage.While the framework for uncertainty quantification of modal parameters is well-known and developed in the context of subspace-based system identification methods, uncertainty quantification for the MAC has not been developed yet. A particular challenge for its statistical characterization is its boundedness in the interval between 0 and 1. In this paper it is shown that this boundedness yields two different distributions of the MAC estimates. The MAC computed between estimates of different mode shapes is inside the interval (0, 1), and a Gaussian approximation of its distribution is obtained. When the MAC is computed between estimates of equal mode shapes, the resultant MAC estimate is close to 1, and the classical Gaussian approximation is inadequate. In this case it is shown that the MAC estimate is linked to a quadratic form of the mode shapes, whose distribution can be approximated by a scaled and shifted χ 2 distribution. For both cases, uncertainty bounds related to the MAC estimates are established. The proposed frameworks are validated by extensive Monte Carlo simulations and then applied to evaluate mode shape changes due to damage during monitoring of the S101 Bridge.

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Section: Applicationmentioning

confidence: 99%

Uncertainty quantification of the Modal Assurance Criterion in operational modal analysis

Greś

Döhler

Mevel

2021

Mechanical Systems and Signal Processing

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“…Among various modal parameter estimation (MPE) techniques, least-squares complex exponential (LSCE) was utilized for MPE using the shake table test results. LSCE approximates the correlation function using the sum of exponentially decaying harmonic functions [13][14][15][16]. After evaluating the modal parameters, the FEM was updated based on test results through a statistical tool, i.e., response surface methodology (RSM).…”

Section: Introductionmentioning

confidence: 99%

Location Sensitivity of Non-structural Component for Channel-type Auxiliary Building Considering Primary-secondary Structure Interaction

2022

IJE

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To ensure the safe and stable operation of nuclear power plants (NPP), many non-structural components (NSCs) are actively associated with NPP. Generally, floor response spectrum (FRS) is used to design the NSCs. Nevertheless, it is essential to focus on the mounting position and frequency of NSCs which is normally ignored during the conventional design of NSCs. This paper evaluates the effect of mounting location for NSCs over the same floor in a channel-type auxiliary building. The modal parameter estimation is taken into account to capture the dynamic property of the NPP auxiliary building by the shake table test; which leads to the calibration of the finite element model (FEM). The calibration of FEM was conducted through response surface methodology (RSM) and the calibrated model is verified utilizing modal parameters as well as frequency response spectrum function. Finally, the location sensitivity was investigated by time history analysis (THA) under artificially generated design response spectrum compatible earthquakes and sine sweeps. The result showed that the right choice of location for NSCs can be an important measure to reduce the undesirable responses during earthquakes, which can reduce up to 30% horizontal and 70% vertical zero period acceleration (ZPA) responses in channeltype auxiliary buildings.

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“…Note that in most machine learning‐based damage detection algorithms for unsupervised classification, the raw monitoring data is transformed using statistical‐based and physical‐based feature extractors, 17,45–48 such as autoregressive models, wavelets, mode shapes and damping ratios (these are the first‐level damage‐sensitive features). Then classification algorithms are used to model the normal structural condition and to detect damage in an unsupervised fashion.…”

Section: Introductionmentioning

confidence: 99%

Damage‐sensitive feature extraction with stacked autoencoders for unsupervised damage detection

Silva

Santos

et al. 2021

Struct Control Health Monit

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Summary In most real‐world monitoring scenarios, the lack of measurements from damaged conditions requires the application of unsupervised approaches, mainly the ones based on modal features estimated from raw vibration data through traditional system identification methods. Although numerous successful applications using modal parameters have been reported, they have demonstrated to be insufficient to estimate a robust set of damage‐sensitive features. Inspired by the idea of compressed sensing and deep learning, an intelligent two‐level feature extraction approach using stacked autoencoders over pre‐processed vibration data is proposed. This procedure can improve the performance of traditional damage detection classifiers by compressing modal parameters into a smaller set of highly informative features when considering information entropy metrics. The proposed technique demonstrates significant improvement in the performance of damage detection and classification approaches when evaluated on the well‐known monitoring data sets from the Z‐24 Bridge, where several damage scenarios were carried out under rigorous operational and environmental effects.

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Modal identification of structures from input/output data using the expectation-maximization algorithm and uncertainty quantification by mean of the bootstrap

Cited by 12 publications

References 33 publications

Uncertainty quantification of the Modal Assurance Criterion in operational modal analysis

Uncertainty quantification of the Modal Assurance Criterion in operational modal analysis

Location Sensitivity of Non-structural Component for Channel-type Auxiliary Building Considering Primary-secondary Structure Interaction

Damage‐sensitive feature extraction with stacked autoencoders for unsupervised damage detection

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