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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Nassehi, Aydin
University of Bristol
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2021The creation of a neural network based capability profile to enable generative design and the manufacture of functional FDM partscitations
- 2021The creation of a neural network based capability profile to enable generative design and the manufacture of functional FDM partscitations
- 2020Transdisciplinary engineering design (TREND)citations
- 2020Transdisciplinary engineering design (TREND):Towards a transdisciplinary engineering indexcitations
- 2014Non-invasive damage detection in beams using marker extraction and waveletscitations
- 2011Non-invasive damage detection in composite beams using marker extraction and waveletscitations
Places of action
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article
Non-invasive damage detection in beams using marker extraction and wavelets
Abstract
For structural health monitoring applications there is a need for simple and contact-less methods of Non-Destructive Evaluation (NDE). A number of damage detection techniques have been developed, such as frequency shift, generalised fractal dimension and wavelet transforms with the aim to identify, locate and determine the severity of damage in a material or structure. These techniques are often tailored for factors such as (i) type of material, (ii) damage pattern (crack, delamination), and (iii) the nature of any input signals (space and time). This paper describes and evaluates a wavelet-based damage detection framework that locates damage on cantilevered beams via NDE using computer vision technologies. The novelty of the approach is the use of computer vision algorithms for the contact-less acquisition of modal shapes. Using the proposed method, the modal shapes of cantilever beams are reconstructed by extracting markers using sub-pixel Hough Transforms from images captured using conventional slow motion cameras. The extracted modal shapes are then used as an input for wavelet transform damage detection, exploiting both discrete and continuous variants. The experimental results are verified and compared against finite element analysis. The methodology enables a non-invasive damage detection system that avoids the need for expensive equipment or the attachment of sensors to the structure. Two types of damage are investigated in our experiments: (i) defects induced by removing material to reduce the stiffness of a steel beam and (ii) delaminations in a (0 / 90 / 0 / 90 / 0) composite laminate. Results show successful detection of notch depths of 5%, 28% and 50% for the steel beam and of 30 mm delaminations in central and outer layers for the composite laminate.