Multiple damage detection in laminated composite beams using automated model update

Kahya, Volkan; Okur, Fatih Yesevi; Karaca, Sebahat; Altunışık, Ahmet Can; Aslan, Mustafa

doi:10.1016/j.istruc.2021.08.117

Cited by 17 publications

(2 citation statements)

References 37 publications

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“…However, the basic requirements are relatively high, and under the complex structure, the ocean platform detection has great limitations. Kahya [8] performed a sensitivity analysis based on the Bayesian parameter estimation and used the model correction method to identify the damage of multi-cracked composite beams. They accurately localized multiple cracks of different severity in cantilever beams, but were less effective in identifying other types of damages of the same severity.…”

Section: Literature Overviewmentioning

confidence: 99%

Research on frequency-variation square ratio in damage identification technique for single column platforms

Sun,

2024

J. vibroeng.

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This paper attempts to utilize lower order harmonics and modes of vibration consisting of “square ratios of frequency variations” as structural damage susceptibility characteristics. This is followed by the development of the frequency-based method. Firstly, this paper derives the theoretical formulation of the square ratio of frequency variation method. Secondly, a finite element model of a single column platform is established. Then the damage is simulated using the reduced modulus of elasticity to localize the damage. The result is shown to be feasible. Thirdly, a model of the offshore single column platform is constructed, and the damage is simulated using cracks on the column. The “frequency variation square ratio” is valid, so the method can be used in reality. This method is applied to the single column platform model, suitable for a beam structure. It is very effective for the single column damage identification at different damage levels, and can also initially locate the damage. This method has high practical application value and reference value for future studies.

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Section: Literature Overviewmentioning

confidence: 99%

Research on frequency-variation square ratio in damage identification technique for single column platforms

Sun,

2024

J. vibroeng.

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“…Zai et al [26] investigated the changes in frequency, damping, and vibration response amplitude under thermomechanical loads and proposed a damage quantification method for cantilever beams. With the development of intelligent algorithms and signal processing technologies, bispectral analysis [27], neural network [27,28], spatial wavelet analysis [29], full wavelet scalogram [28], metaheuristic algorithm [30], Bayesian parameter estimation [31], and deep learning [32] have shown significant promise in the damage detection of cantilever beams.…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Velocity Impact Region Identification Method for Cantilever Beams Using a Support Vector Machine

Wang

Kang

Xiao

et al. 2022

Mathematical Problems in Engineering

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Damage induced by a low-velocity impact can reduce the stability and reliability of structures. In this study, a novel low-velocity impact region identification method based on the spectral peak frequency (SPF) and support vector machine (SVM) is proposed to identify the low-velocity impact regions on a steel cantilever beam. A low-velocity impact region identification system of the cantilever beam is established by applying fiber Bragg grating (FBG) sensors, and only 2 sensors are used in this system. The power spectral density functions of the impact response signal are smoothed using the linear weighting method to remove pseudospectral peak frequencies, and then, SPFs are extracted as the features. For 25 low-velocity impact regions with dimensions of 30 mm × 10 mm, the results show that the recognition rate obtained by the proposed method is 100% and the feature vector consisting of the first two SPFs with the largest amplitude has the highest recognition rate. Through the comparative study, it is found that the recognition rate of SVM is higher than that of the probabilistic neural network (PNN) and extreme learning machine (ELM) for low-velocity impact area recognition of cantilever beams. As a result, the low-velocity impact region identification method of this paper can be applied to the real-time health monitoring of cantilever beam structures.

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Analysis of damage in mono composite leaf spring subjected to multiple cracks scenario

Jamadar¹,

Waghudle²,

Dhummansure³

et al. 2023

Int J Interact Des Manuf

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Multiple damage detection in laminated composite beams using automated model update

Cited by 17 publications

References 37 publications

Research on frequency-variation square ratio in damage identification technique for single column platforms

Research on frequency-variation square ratio in damage identification technique for single column platforms

A Novel Low-Velocity Impact Region Identification Method for Cantilever Beams Using a Support Vector Machine

Analysis of damage in mono composite leaf spring subjected to multiple cracks scenario

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