An enhanced energy vibration-based approach for damage detection and localization

Tributsch, Alexander; Adam, Christoph

doi:10.1002/stc.2047

Cited by 25 publications

(10 citation statements)

References 39 publications

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“…Next, the regularization coefficient is determined through the method proposed above. After adding the regularization coefficient, the regularized ARMA (25,24) model is used to fit two similar virtual pulse response signals under the same damage states. The regularization coefficient increases gradually from 0.002 to 0.1, with each increase set to 0.002, for a total of 50 groups of data.…”

Section: Damage Identification Processmentioning

confidence: 99%

“…The non-modal parametric methods [21][22][23] use the structural vibration response data directly or extract information from the measured response data that can reflect structural damage through a certain transformation. Compared to the structural modal parametric methods, the non-modal parametric methods are convenient, accurate, and can meet the real-time requirements of structural health monitoring [24].…”

Section: Introductionmentioning

confidence: 99%

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Structure Damage Identification Based on Regularized ARMA Time Series Model under Environmental Excitation

Zhang

Song

2018

Vibration

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In this paper, a non-modal parametric method to identify structural damage using a regularized autoregressive moving average time series model under environmental excitation is proposed in combination with the virtual impulse response function. This method can use the structural vibration response to determine the damage caused to the structure during environmental excitation. Firstly, the virtual impulse response function is obtained by using the structural vibration response. Then, a regularized ARMA time series model is used to fit the virtual impulse response function. Based on the change of auto-regression coefficients in the regularization model under different damage cases, the structural damage is identified. The authors derive the regularization equation of an ARMA time series model to solve the problems in a time series model and obtain the regularization coefficient. Finally, this method is applied to a three-degrees-of-freedom chain structure and a three-floor shear structure of the Los Alamos National Laboratory (LANL). The experimental results show that the method based on the regularized ARMA time series model under environmental excitation can effectively identify the structural damage, which is a reliable method for damage identification. The regularized ARMA time series model can accurately extract signal features and has invaluable application prospects in the field of structural health monitoring.

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Section: Damage Identification Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structure Damage Identification Based on Regularized ARMA Time Series Model under Environmental Excitation

Zhang

Song

2018

Vibration

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“…Many structural health monitoring (SHM) methods focus on detecting structural damage via system identification methods (Billmaier and Bucher, 2013; Chang and Loh, 2015; Tributsch and Adam, 2018; Worden et al, 2008). Structural damage is detected, located and even quantified by tracking the changes of estimated structural parameters at different stages.…”

Section: Introductionmentioning

confidence: 99%

Multi-taper method based substructure identification for shear structures

Zhang

Huang

et al. 2020

Journal of Vibration and Control

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Quickly and accurately identifying structural status after natural disasters plays crucial roles in disaster rescue. Previously, the authors developed a substructure identification method for shear structures, which uses the frequency responses of short structural acceleration responses to estimate structural parameters inductively. However, the numerical studies found that the method could only provide moderately accurate results. In this paper, a thorough uncertainty analysis is performed to reveal the key factors that influence its identification accuracy. Based on these results, a new substructure method is proposed herein, which utilizes the cross power spectrum densities of structural responses, estimated by the multi-taper method, to formulate substructure identification problems. The error analysis is also conducted for the multi-taper method based method, explaining why this method can significantly improve identification accuracy, compared with the frequency response based method. Moreover, although the multi-taper method based method is originally derived based on stationary structural responses, a further analysis shows that it can be extended to non-stationary responses, greatly broadening the method’s application range. Finally, the simulation study of a 20-story shear structure and the shake table tests on a three-story bench-scaled structure are conducted, which verified that the proposed multi-taper method based method indeed significantly improves the substructure identification accuracy.

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“…From this transfer function matrix, the modal parameters (resonance frequencies, damping ratios and mode shapes) are identified. Once a performance indicator has been chosen (either one of the modal parameters or a post-processing variable such as the mode shape curvature (Pandey, Biswas, & Samman, 1995) or the normalized cumulative PSD (Tributsch & Adam, 2018)), in theory a change in this indicator value is associated with a change in system stiffness, which in many cases indicates deterioration or damage to the structure.…”

Section: Introductionmentioning

confidence: 99%

Variable mass loading effect on the long-term ambient response of a freeway bridge

Furtmüller

Adam

Veit‐Egerer

2020

Structure and Infrastructure Engineering

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This study addresses the quantification and compensation of the so-called mass loading effect, a disturbance that typically occurs in structural health monitoring of bridges. Source of this effect is the time-variant traffic, which means that dynamic performance indicators such as identified resonance frequencies are also time-variant. Basis of this study is the ambient acceleration response of a freeway bridge in the Alps recorded in a long-term monitoring campaign. After statistical evaluation of the traffic over the bridge, stochastic finite element simulations are performed to quantify the effect of timevariant truck traffic on a resonance frequency identified from the recorded accelerations, which serves as performance indicator. The corresponding natural frequency of the numerical bridge-truck model is obtained both by modal analyses and response history analyses, whose results however show considerable differences. In an effort to reduce the temporal scatter of the performance indicator, the corresponding natural frequency of the numerical model is used to compensate for the mass loading effect. It it shown that the mass loading effect plays a significant role for the considered bridge, although temperature effects dominate as a disturbance for the identified resonance frequency.

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An enhanced energy vibration-based approach for damage detection and localization

Cited by 25 publications

References 39 publications

Structure Damage Identification Based on Regularized ARMA Time Series Model under Environmental Excitation

Structure Damage Identification Based on Regularized ARMA Time Series Model under Environmental Excitation

Multi-taper method based substructure identification for shear structures

Variable mass loading effect on the long-term ambient response of a freeway bridge

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