Vibration-based damage identification of plate structures via curvelet transform

Bagheri, Abdollah; Amiri, G. Ghodrati; Razzaghi, Seyed Ali Seyed

doi:10.1016/j.jsv.2009.06.019

Cited by 32 publications

(16 citation statements)

References 16 publications

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“…The First group are approaches in which a direct methodology is introduced for damage detection. Such an aim was achieved by performing signal processing-based procedures [2] and mathematical analysis [3]. The Second group are defining damage detection problem as an inverse problem and solving it by employing optimization procedures [4,5].…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Detection Based on Improved Multi-Particle Swarm Co-Evolution Optimization Algorithm

Jiang¹,

Ma²

2017

Structural Health Monitoring - Measurement Methods and Practical Applications

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This chapter presents an improved multi-particle swarm co-evolution optimization algorithm (IMPSCO) to detect structural damage. Firstly, IMPSCO is integrated with Newmark's algorithm for damage detection and system identification, which just need few sensors. In addition, for reducing the searching parameters, a two-stage damage detection method based on modal strain energy and IMPSCO is presented. In order to validate the proposed method, a seven-story steel frame experiment in laboratory conditions is performed and a comparison is made between the proposed approach and genetic algorithm (GA). The results show that: (1) the proposed methods can not only effectively locate damage location but also accurately quantify the damage severity. Besides, it has excellent noise-tolerance and adaptability; (2) for integrating IMPSCO and Newmark'salgorithm,it implements only by using any kinds of structural time-series responses and the excitation force; (3) compared with genetic algorithm, IMPSCO is more efficient and robust for damage detection with a better noise-tolerance.

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Section: Introductionmentioning

confidence: 99%

Structural Damage Detection Based on Improved Multi-Particle Swarm Co-Evolution Optimization Algorithm

Jiang¹,

Ma²

2017

Structural Health Monitoring - Measurement Methods and Practical Applications

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“…Recently, the improvement of wavelet-based SDI algorithms has been based on adaptation novel transforms and hybridization of the existing ones with supporting computational methods including the approaches based on softcomputing and artificial intelligence. An effective approach in SDI problems was proposed by Bagheri et al [47], who used a discrete curvelet transform in order to detect a crack-like surface structural damage. Several studies on improvement of SDI algorithm were performed by the author's team.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Damage Assessment of Composite Structures Based on Wavelet Analysis of Modal Curvatures: State-of-the-Art Review and Description of Wavelet-Based Damage Assessment Benchmark

Katunin

2015

Shock and Vibration

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The application of composite structures as elements of machines and vehicles working under various operational conditions causes degradation and occurrence of damage. Considering that composites are often used for responsible elements, for example, parts of aircrafts and other vehicles, it is extremely important to maintain them properly and detect, localize, and identify the damage occurring during their operation in possible early stage of its development. From a great variety of nondestructive testing methods developed to date, the vibration-based methods seem to be ones of the least expensive and simultaneously effective with appropriate processing of measurement data. Over the last decades a great popularity of vibration-based structural testing has been gained by wavelet analysis due to its high sensitivity to a damage. This paper presents an overview of results of numerous researchers working in the area of vibration-based damage assessment supported by the wavelet analysis and the detailed description of the Wavelet-based Structural Damage Assessment (WavStructDamAs) Benchmark, which summarizes the author’s 5-year research in this area. The benchmark covers example problems of damage identification in various composite structures with various damage types using numerous wavelet transforms and supporting tools. The benchmark is openly available and allows performing the analysis on the example problems as well as on its own problems using available analysis tools.

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“…Several approaches have been developed to extract information from the measured vibrational characteristics of structures, which fall in a category well known as ''vibration-based structural health monitoring (SHM)'' [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. These methods essentially utilize natural frequencies and mode shapes acquired from different types of physical sensors such as accelerometers.…”

Section: Introductionmentioning

confidence: 99%

Eulerian-based virtual visual sensors to detect natural frequencies of structures

Shariati

Schumacher

Ramanna

2015

J Civil Struct Health Monit

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Natural frequency of vibration data are often used to study the behavior of structures. They are also used to calibrate finite element models and some studies have proposed that the presence and location of damage can be estimated using these data. Along this line, we earlier proposed the concept of Eulerian-based virtual visual sensors to estimate natural frequencies of structural vibrations based on the change of pixel intensity captured in a digital video. Benefits of this approach are that it allows for distributed sensing and is contactless. However, as intensity does not reflect any physical quantity, such as displacement, and the range of values is difficult to control, the signal-to-noise ratio (SNR) can be relatively low. Furthermore, impulsive changes of intensity caused by large deformations compared to the pixel size can result in an impulse train in the frequency domain which leads to ambiguity in determining peak frequencies. As a result, it is often only possible to estimate the first fundamental mode of vibration. In this paper, we present strategies using targets mounted to the structure combined with signal processing methods that significantly improve the SNR and allow for detecting higher natural frequencies of vibration. The concepts, their mathematical background, laboratory tests to prove the accuracy and enhancement of SNR, as well as an example of an in-service pedestrian bridge are presented and discussed.

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Vibration-based damage identification of plate structures via curvelet transform

Cited by 32 publications

References 16 publications

Structural Damage Detection Based on Improved Multi-Particle Swarm Co-Evolution Optimization Algorithm

Structural Damage Detection Based on Improved Multi-Particle Swarm Co-Evolution Optimization Algorithm

Nondestructive Damage Assessment of Composite Structures Based on Wavelet Analysis of Modal Curvatures: State-of-the-Art Review and Description of Wavelet-Based Damage Assessment Benchmark

Eulerian-based virtual visual sensors to detect natural frequencies of structures

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