4.7 Article

Damage identification in beams using burst video-records during free-vibration

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ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2023.110539

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Damage identification; Free-vibration; Time domain; Computer vision; Beam; Camera based

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In this paper, a new method for damage identification in beams is proposed, which is vibration-based and Output-Only, but does not rely on Modal or Fourier analysis. The method uses the Synthesis of the Healthy-Structure Model Response and interprets the accelerations in free-vibration as an inertial pseudo-loading. The results of an experimental example show outstanding detection and localization performance with low quantification error.
ABS T R A C T In Structural Health Monitoring, computer vision has emerged as a promising approach for dynamic measurement; especially for its full-field characteristic. However, real-world imple-mentations are still sparse due to several reasons. One of them is that vibration-based damage identification methods usually rely on Output-Only Modal-Analysis which, to be accurate, requires very long records with stochastic excitation or relatively long free-vibration records. The first option hinders video taking, storage and processing; specially with high-speed cameras. The second is statistically difficult under common operating conditions. To overcome such difficulties, this paper presents a methodology for damage identification in beams; which is vibration-based and Output-Only, but does not rely on Modal (or other Fourier-based) Analysis. Therefore, it can be fed with very short free-vibration displacement time histories; e.g., extracted from burst video records. The key idea is using the recently developed Synthesis of the Healthy-Structure Model Response, but interpreting the accelerations in free-vibration as an inertial pseudo-loading. The likely problem of high levels of noise is addressed by separating damage detection and localisation (using a statistical approach) from damage quantification (which is physics based). In an experimental example, the methodology showed outstanding detection and localisation performance with low quantification error.

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