A refined Frequency Domain Decomposition tool for structural modal monitoring in earthquake engineering

Pioldi, Fabio; Rizzi, Egidio

doi:10.1007/s11803-017-0394-9

Cited by 18 publications

(20 citation statements)

References 12 publications

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“…As opposed to rFDD attempts in previous studies() with pseudo‐experimental (synthetic) seismic response signals, this original integrated procedure leads to a substantial improvement of the seismic modal estimates in case of real earthquake input, as analysed here. In fact, for real seismic response signals, this procedure is automatically and systematically performed by the rFDD algorithm.…”

Section: Fundamentals Of the Rfdd Algorithm With Reference To Real Sementioning

confidence: 94%

“…A first theoretical demonstration of the rFDD efficacy has been reported in previous study. () In one study,() the instantaneous Auto‐Correlation Functions of the seismic records have been involved, too. Both studies referred only to synthetic earthquake response signals, in view of a necessary validation condition for the identification technique.…”

Section: Fundamentals Of the Rfdd Algorithm With Reference To Real Sementioning

confidence: 99%

“…the first two statistical moments (mean and autocorrelation) of the signals do not significantly vary in time. In one study,() the reliability of this assumption was demonstrated for an applicative seismic case. Accordingly, over this frequency range, the PSD can be approximately represented as to be merely frequency dependent, allowing to follow classical PSD computations.…”

Section: Fundamentals Of the Rfdd Algorithm With Reference To Real Sementioning

confidence: 99%

“…The Wiener–Khinchin algorithm is based on the direct Fourier Transform (FT) of the correlation matrix

{boldR}_{y y}^{detr} false(τ false)

, in order to obtain the PSD matrix of the responses

{boldG}_{y y}^{detr} false(ω_{S u b} false)

\begin{matrix} right {boldG}_{y y}^{detr} (ω_{S u b}) & left = ℱ [{boldR}_{y y}^{detr} (τ)] & right \\ right & left = ℱ [\overset{true‾}{normalΦ} {boldR}_{p p}^{detr} (τ) {normalΦ}^{normalT}] \\ right & left = \overset{true‾}{normalΦ} {boldG}_{p p}^{detr} (ω_{S u b}) {normalΦ}^{normalT}, \end{matrix}

where

{boldG}_{p p}^{detr} false(ω_{S u b} false) = ℱ ([], R_{p p}^{detr} false(τ false))

is the response PSD matrix (in terms principal coordinates p i ), obtained as FT of the untrended correlation matrix

{boldR}_{p p}^{detr} false(τ false)

(see previous study() for more details), expressed again in principal coordinates. Then, this method shall be called Correlation Approach (Corr) .…”

Section: Fundamentals Of the Rfdd Algorithm With Reference To Real Sementioning

confidence: 99%

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Seismic FDD modal identification and monitoring of building properties from real strong-motion structural response signals

Pioldi

Ferrari

Rizzi

2017

Struct Control Health Monit

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Summary In the present study, output‐only modal dynamic identification and monitoring of building properties is attempted successfully by processing real earthquake‐induced structural response signals. This is achieved through an enhanced version of a recently‐developed refined Frequency Domain Decomposition (rFDD) approach, which in the earlier implementation was adopted to analyse synthetic seismic response signals only. Despite that short duration, nonstationary seismic response data and heavy structural damping shall not fulfil traditional Operational Modal Analysis assumptions, the present rFDD response‐only algorithm allows for the effective estimation of strong‐motion natural frequencies, mode shapes, and modal damping ratios, with real seismic response signals. The present rFDD enhancement derives from a preprocessing time‐frequency analysis and from an integrated approach for Power Spectral Density matrix computation, which constitute crucial innovative issues for the treatment of real earthquake response data. A monitoring case study is analysed by taking the real strong‐motion response records from a seven‐storey reinforced concrete building in Van Nuys, California, from 1987 to the latest 2014 events (Center of Engineering Strong Motion Data database), as recorded before, during and after the 1994 Northridge earthquake, which severely damaged the building (then retrofitted). This paper proves the effectiveness of the proposed enhanced rFDD algorithm as a robust method for monitoring current structural modal properties under real earthquake excitations. This shall allow for identifying possible variations of structural features along experienced seismic histories, providing then a fundamental tool towards Earthquake Engineering and Structural Health Monitoring purposes.

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Section: Fundamentals Of the Rfdd Algorithm With Reference To Real Sementioning

confidence: 94%

Section: Fundamentals Of the Rfdd Algorithm With Reference To Real Sementioning

confidence: 99%

Section: Fundamentals Of the Rfdd Algorithm With Reference To Real Sementioning

confidence: 99%

“…The Wiener–Khinchin algorithm is based on the direct Fourier Transform (FT) of the correlation matrix

{boldR}_{y y}^{detr} false(τ false)

, in order to obtain the PSD matrix of the responses

{boldG}_{y y}^{detr} false(ω_{S u b} false)

\begin{matrix} right {boldG}_{y y}^{detr} (ω_{S u b}) & left = ℱ [{boldR}_{y y}^{detr} (τ)] & right \\ right & left = ℱ [\overset{true‾}{normalΦ} {boldR}_{p p}^{detr} (τ) {normalΦ}^{normalT}] \\ right & left = \overset{true‾}{normalΦ} {boldG}_{p p}^{detr} (ω_{S u b}) {normalΦ}^{normalT}, \end{matrix}

where

{boldG}_{p p}^{detr} false(ω_{S u b} false) = ℱ ([], R_{p p}^{detr} false(τ false))

is the response PSD matrix (in terms principal coordinates p i ), obtained as FT of the untrended correlation matrix

{boldR}_{p p}^{detr} false(τ false)

(see previous study() for more details), expressed again in principal coordinates. Then, this method shall be called Correlation Approach (Corr) .…”

Section: Fundamentals Of the Rfdd Algorithm With Reference To Real Sementioning

confidence: 99%

See 2 more Smart Citations

Seismic FDD modal identification and monitoring of building properties from real strong-motion structural response signals

Pioldi

Ferrari

Rizzi

2017

Struct Control Health Monit

Self Cite

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“…By making reference to such an OMA field, the present work adopts earthquake-induced structural output responses as input channels for a refined Frequency Domain Decomposition (rFDD) algorithm 10 and an improved Data-Driven Stochastic Subspace Identification (SSI-DATA) technique. 11 Both have been implemented autonomously within MATLAB, for rFDD in works [12][13][14] and for SSI-DATA starting from works, 15,16 and as finally completed here. The present implementations have been specifically developed to deal with short-duration, non-stationary, earthquake-induced structural response signals and simultaneous heavy damping (in terms of identification challenge, i.e., with modal damping ratios larger than only a few percents and up to 10% or even higher).…”

Section: Introductionmentioning

confidence: 99%

Earthquake‐induced structural response output‐only identification by two different Operational Modal Analysis techniques

Pioldi

Rizzi

2017

Earthq Engng Struct Dyn

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SummaryOutput-only system identification is developed here towards assessing current modal dynamic properties of buildings under seismic excitation. Earthquake-induced structural response signals are adopted as input channels for two different Operational Modal Analysis (OMA) techniques, namely, a refined Frequency Domain Decomposition (rFDD) algorithm and an improved Data-Driven Stochastic Subspace Identification (SSI-DATA) procedure. Despite that short-duration, non-stationary, earthquake-induced structural response signals shall not fulfil traditional OMA assumptions, these implementations are specifically formulated to operate with seismic responses and simultaneous heavy damping (in terms of identification challenge), for a consistent estimation of natural frequencies, mode shapes, and modal damping ratios. A linear ten-storey frame structure under a set of ten selected earthquake base-excitation instances is numerically simulated, by comparing the results from the two identification methods. According to this study, best up-to-date, reinterpreted OMA techniques may effectively be used to characterize the current dynamic behaviour of buildings, thus allowing for potential Structural Health Monitoring approaches in the Earthquake Engineering range.

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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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A refined Frequency Domain Decomposition tool for structural modal monitoring in earthquake engineering

Cited by 18 publications

References 12 publications

Seismic FDD modal identification and monitoring of building properties from real strong-motion structural response signals

Seismic FDD modal identification and monitoring of building properties from real strong-motion structural response signals

Earthquake‐induced structural response output‐only identification by two different Operational Modal Analysis techniques

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

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