Fatigue life prediction of sandwich composite materials under flexural tests using a Bayesian trained artificial neural network

Bezazi, A. and Pierce, S.G. and Worden, K. and Harkati, E. (2007) Fatigue life prediction of sandwich composite materials under flexural tests using a Bayesian trained artificial neural network. International Journal of Fatigue, 29 (4). pp. 738-747. ISSN 0142-1123 (https://doi.org/10.1016/j.ijfatigue.2006.06.013)

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Abstract

The authors discuss the use of an artificial neural network (ANN) to estimate fatigue lifetime of a sandwich composite material structure subjected to cyclic three-point bending loads. A total of 27 samples (three different loading levels for nine samples each) were investigated to provide training, validation and testing data for a series of multi-layer perceptron ANNs. The networks were implemented using both conventional maximum likelihood and Bayesian evidence based training algorithms. It was found that the Bayesian evidence based approach provided a superior and smoother fit to the experimental data. Completely independent fatigue tests were conducted at intermediate levels of loading to evaluate the capacity of the fitted ANN model to interpolate to previously unseen regions of data. Excellent agreement was obtained between the model predicted outputs and the new experimental data. The capability and estimation of predication error when using the Bayesian technique is discussed along with the application of the model to generate classical S–N lifetime curves.

ORCID iDs

Bezazi, A., Pierce, S.G. ORCID logoORCID: https://orcid.org/0000-0003-0312-8766, Worden, K. and Harkati, E.;
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