A bayesian artificial neural network method to characterise laminar defects using dynamic measurements

Lam, H. F., Veidt, M. and Kitipornchai, S. (2004). A bayesian artificial neural network method to characterise laminar defects using dynamic measurements. In: L. Ye, Y-W. Mai and Z. Su, Proceedings of the Fourth-Asian-Australasian Conference on Composite Materials. Composites Technologies for 2020, Sydney, Australia, (975-980). 6-9 July, 2004. doi:10.1016/B978-1-85573-831-7.50164-4


Author Lam, H. F.
Veidt, M.
Kitipornchai, S.
Title of paper A bayesian artificial neural network method to characterise laminar defects using dynamic measurements
Conference name Composites Technologies for 2020
Conference location Sydney, Australia
Conference dates 6-9 July, 2004
Proceedings title Proceedings of the Fourth-Asian-Australasian Conference on Composite Materials
Journal name Composites Technologies For 2020
Place of Publication Australia
Publisher The Asian-Australasian Association for Composite Materials, University of Sydney
Publication Year 2004
Sub-type Fully published paper
DOI 10.1016/B978-1-85573-831-7.50164-4
ISBN 9781855738317
9781845690625
Editor L. Ye
Y-W. Mai
Z. Su
Start page 975
End page 980
Total pages 6
Collection year 2004
Language eng
Abstract/Summary This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.
Subjects E1
290501 Mechanical Engineering
671401 Scientific instrumentation
Q-Index Code E1

 
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Created: Thu, 23 Aug 2007, 19:45:00 EST