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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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Chinchilla, Sergio Cantero
University of Bristol
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2024Uncertainty quantification of damage localization based on a probabilistic convolutional neural networkcitations
- 2021Bayesian damage localization and identification based on a transient wave propagation model for composite beam structurescitations
- 2021Structural health monitoring using ultrasonic guided-waves and the degree of health indexcitations
- 2021A homogenisation scheme for ultrasonic Lamb wave dispersion in textile composites through multiscale wave and finite element modellingcitations
- 2020Ultrasonic guided wave testing on cross-ply composite laminatecitations
- 2020A fast Bayesian inference scheme for identification of local structural properties of layered composites based on wave and finite element-assisted metamodeling strategy and ultrasound measurementscitations
- 2017A multilevel Bayesian method for ultrasound-based damage identification in composite laminatescitations
Places of action
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article
Bayesian damage localization and identification based on a transient wave propagation model for composite beam structures
Abstract
<p>This paper proposes the use of a physics-based Bayesian framework for the localization and identification of damage in composite beam structures using ultrasonic guided-waves. The methodology relies on a transient wave propagation model based on wave and finite element scheme that efficiently provides time-domain signals that are compared with the ultrasonic measurements within a multilevel Bayesian framework. As a key contribution, the proposed methodology enables the localization and identification of damage using just the signals without any baseline comparison or further transformation, hence reducing additional sources of uncertainty. The proposed Bayesian approach allows (1) the localization of the defect, and (2) the identification of different candidate damage hypotheses and their ranking based on probabilities that measure their relative degree of belief. The methodology is illustrated in carbon fiber reinforced polymer beams with different layups. An investigation into how the measurement noise can impact the identified damage properties is also provided. The results show the effectiveness and efficiency of the proposed approach in reconstructing and identifying different types of damage in long and complex composite beams at a relatively low computational cost.</p>