Underdetermined blind modal identification of structures by earthquake and ambient vibration measurements via sparse component analysis

Amini, Fereidoun; Hedayati, Yousef

doi:10.1016/j.jsv.2015.10.028

Cited by 51 publications

(50 citation statements)

References 32 publications

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“…In addition, the obtained results allow to affirm that the damping ratios values estimated by the proposal are more accurate than the ones calculated by Amini and Hedayati [35] and Perez-Ramirez et al [11], since a maximum error for the proposal and the other two works are: 6%, 12% and 15%, respectively. In particular, the proposal outperforms the methodology of the former reference by achieving an error reduction of over 50%.…”

Section: Numerical Assessment Of the New Methodology Performancesupporting

confidence: 67%

“…On the contrary, the methodology of Perez-Ramirez et al [11] is more susceptible to these spikes, as the value is an integral part of the equations used to obtain the DR value. It can be seen that the approach used by Amini and Hedayati [35], show that the used methodology can successfully estimate the DR with a reasonable accuracy, indicating that their methodology has a better performance to estimate the aforementioned values. The main objective of Li et al [34] is to find the best FE-based model; thus, the authors do not estimate the damping ratios of structure.…”

Section: Numerical Assessment Of the New Methodology Performancementioning

confidence: 91%

“…For the first one, the benchmark of the International Association for Structural Control American Society of Civil Engineers Structural Health Monitoring (IASC-ASCE SHM) Task Group, which consists of a four-story 2 × 2 bay 3D steel frame structure is used, where the simulated response obtained from a finite element analysis (FEA) model is processed to obtain its modal parameters. The obtained results are compared with other methodologies [11,34,35]. Regarding the second study case, the real measurements obtained from an eight-story steel frame subjected to white noise input in a shaking table test are also processed to estimate its modal parameters.…”

Section: Methodsmentioning

confidence: 99%

“…On the contrary, the results estimated by the former show Table 2 summarizes modal parameters calculated using the proposal and the ones estimated by the FEA model. Additionally, the estimated natural frequencies and damping ratios using the methodologies proposed by Li et al [34], Amini and Hedayati [35], and Perez-Ramirez et al [11] are presented. In general, the results presented in this table show that the proposal can identify and estimate the first eight modes detect by the FEM model in an accurate way.…”

Section: Numerical Assessment Of the New Methodology Performancementioning

confidence: 99%

“…Even when the maximum deviation (0.33%) is lower than the one obtained with this proposal, more NF modes have a significant lower deviation compared with the aforementioned methodology. The work reported by Amini and Hedayati [35] use the sparse component analysis, a blind source separation technique, to analyze the response. It is evident that they cannot estimate the third NF of the y-direction.…”

Section: Numerical Assessment Of the New Methodology Performancementioning

confidence: 99%

See 4 more Smart Citations

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

et al. 2017

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Nowadays, the accurate identification of natural frequencies and damping ratios play an important role in smart civil engineering, since they can be used for seismic design, vibration control, and condition assessment, among others. To achieve it in practical way, it is required to instrument the structure and apply techniques which are able to deal with noise-corrupted and non-linear signals, as they are common features in real-life civil structures. In this article, a two-step strategy is proposed for performing accurate modal parameters identification in an automated manner. In the first step, it is obtained and decomposed the measured signals using the natural excitation technique and the synchrosqueezed wavelet transform, respectively. Then, the second step estimates the modal parameters by solving an optimization problem employing a genetic algorithm-based approach, where the micropopulation concept is used to improve the speed convergence as well as the accuracy of the estimated values. The accuracy and effectiveness of the proposal are tested using both the simulated response of a benchmark structure and the measurements of a real eight-story building. The obtained results show that the proposed strategy can estimate the modal parameters accurately, indicating than the proposal can be considered as an alternative to perform the abovementioned task.

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Section: Numerical Assessment Of the New Methodology Performancesupporting

confidence: 67%

Section: Numerical Assessment Of the New Methodology Performancementioning

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Section: Numerical Assessment Of the New Methodology Performancementioning

confidence: 99%

Section: Numerical Assessment Of the New Methodology Performancementioning

confidence: 99%

See 3 more Smart Citations

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

et al. 2017

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Blind modal identification of non-classically damped structures under non-stationary excitations

Ghahari

Abazarsa

Taciroğlu

2016

Struct. Control Health Monit.

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Summary In the present paper, we extend a previously developed blind modal identification method to systems with non‐classical damping, which have complex‐valued mode shapes. Unlike conventional output‐only identification methods, blind modal identification method can provide modal property estimates when the input excitations are unknown and non‐stationary (e.g., for systems equipped with added dampers or soil–structure problems). In earlier work, we have developed a technique that can be used for non‐classically damped systems, but it is only applicable to stationary (i.e., ambient) excitations. Herein, we present an extension to non‐classically damped systems under non‐stationary (e.g., seismic) excitations. The proposed method yields mode shapes, natural frequencies and damping ratios, sequentially. A critical ingredient in this new method is a combination of generalized eigen‐decomposition and rough–fuzzy c‐means clustering techniques, which are employed to identify the complex‐valued mode shapes. The accuracy of the proposed method is verified through a simulated but realistic example. Copyright © 2016 John Wiley & Sons, Ltd.

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Machine‐learning‐based methods for output‐only structural modal identification

Liu

Tang

Bao

et al. 2021

Struct Control Health Monit

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Underdetermined blind modal identification of structures by earthquake and ambient vibration measurements via sparse component analysis

Cited by 51 publications

References 32 publications

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

Blind modal identification of non-classically damped structures under non-stationary excitations

Machine‐learning‐based methods for output‐only structural modal identification

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