Detecting defects in composite beams and plates using Bayesian inference

Date
2014-10-15
Authors
Chung, H
Lee, J
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Item type
Conference Contribution
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Publisher
KU Leuven - Department of Mechanical Engineering
Abstract

The topic of this paper is an inverse problem of identifying defects in composite beams and plates. The physical representation of defects is parametrized. Assuming Gaussian errors in measurements, the Bayesian inference is performed for those unknown parameters, and the most probable physical representations of detects are estimated. A composite beam/plate is usually made up of several layers, and there may be some defect in the bonding process or a defect may develop later. We use the natural frequencies of the beam/plate to estimate the position and the size of the defects. We propose that the bonding within the beam and plate can be modelled as added rigidity, which can be incorporated as an extra energy to the conventional strain energy. Standard Monte-Carlo simulation will then give the probabilistic properties of the natural frequencies of the beam/plate. The more prior information about the defects is limited, and thus we estimate the posterior distribution using the trans-dimensional Bayesian method, which lets us make an inference of different types of defects.

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Source
International Conference on Noise and Vibration Engineering, ISMA held at Leuven Belgium, Leuven Belgium, 2014-09-15 to 2014-09-17, published in: Proceedings of the International Conference on Noise and Vibration Engineering, ISMA
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NOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version).