An effective approach for structural damage localization in flexural members based on generalized S-transform

Tehrani, Hamed Amini; Bakhshi, Ali; Akhavat, Moustafa

doi:10.24200/sci.2017.20019

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…The key point is that there are many effective damage detection techniques in the literature, which are suitable for rectangular structures in the Cartesian coordinates [7][8][9][10][11][12]. Most of these techniques are not directly applicable to circular structures, and it is also very difficult to extend them to the polar coordinates.…”

Section: Introductionmentioning

confidence: 99%

Damage Diagnosis in Circular Structures using Cartesian Wavelet Analysis: A Comparison between Two Structural Signals

et al. 2022

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Circular structures are used in a wide variety of engineering mechanisms and devices. In this paper, an effective algorithm on the basis of the complex mappings is proposed to identify defects in circular structures using Cartesian damage detection techniques. The efficacy of the proposed algorithm is demonstrated through damage identification in circular plates using the Cartesian wavelet analysis. The vibration and thermal responses of the structure, as two important structural signals, are imported into the proposed algorithm to evaluate the abilities of the signals in identifying the damage location and severity. Finally, two experimental tests are conducted to explore the efficacy of the proposed algorithm in real applications.

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Section: Introductionmentioning

confidence: 99%

Damage Diagnosis in Circular Structures using Cartesian Wavelet Analysis: A Comparison between Two Structural Signals

et al. 2022

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“…The experimental results showed that the proposed method had high accuracy. Tehrani [22] utilized an artificial intelligence method based on fast Fourier transform (FFT) to recognize structural damage degree. The results showed that FFT was suitable for nonstationary vibration signals.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

Chen

Liu

et al. 2021

Applied Sciences

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In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

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“…In doing so, higher frequency resolutions are brought, and a uniform resolution for all scales is provided [15]. However, WT and SFFT have limitations in Time-Frequency representation of non-stationary signals [16]. Huang et al [17,18] rstly developed a new method{Hilbert Huang Transformation (HHT){in order to analyze both nonlinear and non-stationary signals.…”

Section: Introductionmentioning

confidence: 99%

Online Nonlinear Structural Damage Detection Using Hilbert Huang Transform and Artificial Neural Networks

2019

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In order to implement a damage detection strategy and assess the condition of a structure, Structural Health Monitoring (SHM) as a process plays a key role in structural reliability. This paper aims to present a methodology for online detection of damages that may occur during a strong ground excitation. In this regard, Empirical Mode Decomposition (EMD) is superseded by Ensemble Empirical Mode Decomposition (EEMD) in the Hilbert Huang Transformation (HHT). Although analogous with EMD, EEMD brings about more appropriate Intrinsic Mode Functions (IMFs). IMFs are employed to assess the rst-mode frequency and mode shape. Afterwards, Arti cial Neural Network (ANN) is applied to predict story acceleration based on previously measured values. Because ANN functions precisely, any congruency between predicted and measured accelerations indicates the onset of damage. Then, another ANN method is applied to estimate the sti ness matrix. Though the rst-mode shape and frequency are calculated in advance, the process essentially requires an inverse problem to be solved in order to nd sti ness matrix, which is done by ANN. This algorithm is implemented on moment-resisting steel frames, and the results show the reliability of the proposed methodology for online prediction of structural damage.

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An effective approach for structural damage localization in flexural members based on generalized S-transform

Cited by 4 publications

References 24 publications

Damage Diagnosis in Circular Structures using Cartesian Wavelet Analysis: A Comparison between Two Structural Signals

Damage Diagnosis in Circular Structures using Cartesian Wavelet Analysis: A Comparison between Two Structural Signals

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

Online Nonlinear Structural Damage Detection Using Hilbert Huang Transform and Artificial Neural Networks

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