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Vibration-based damage identification in beam-like composite laminates by using artificial neural networks

Sahin, M. and Shenoi, R.A. (2003) Vibration-based damage identification in beam-like composite laminates by using artificial neural networks Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 217, (6), pp. 661-676. (doi:10.1243/095440603321919581).

Record type: Article

Abstract

This paper investigates the effectiveness of the combination of global (changes in natural frequencies) and local (curvature mode shapes) vibration-based analysis data as input for artificial neural networks (ANNs) for location and severity prediction of damage in fibre-reinforced plastic laminates. A finite element analysis tool has been used to obtain the dynamic characteristics of intact and damaged cantilever composite beams for the first three natural modes. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the finite element model of the beam structure. After performing the sensitivity analyses aimed at finding the necessary parameters for the damage detection, different input–output sets have been introduced to various ANNs. In order to check the robustness of the input used in the analysis, random noise has been generated numerically and added to noise-free data during the training of the ANNs. Finally, trained feedforward back propagation ANNs have been tested using new damage cases and checks have been made for severity and location prediction of the damage.

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More information

Published date: 2003
Keywords: vibration-based analysis, curvature mode shape, artificial neural networks, damage quantification, damage localization, composite structures, fine element modelling, noise

Identifiers

Local EPrints ID: 22434
URI: http://eprints.soton.ac.uk/id/eprint/22434
ISSN: 0954-4062
PURE UUID: 370a768a-dbfa-47c1-9115-e277dcd731bd

Catalogue record

Date deposited: 20 Mar 2006
Last modified: 17 Jul 2017 16:22

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Contributors

Author: M. Sahin
Author: R.A. Shenoi

University divisions

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