Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 738-747 |
| Number of pages | 10 |
| Journal | International Journal of Fatigue |
| Volume | 29 |
| Issue number | 4 |
| Early online date | 6 Sep 2006 |
| DOIs | |
| Publication status | Published - Apr 2007 |
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Keywords
- sandwich composites
- fatigue
- neural networks
- lifetime prediction
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Fatigue life prediction of sandwich composite materials under flexural tests using a Bayesian trained artificial neural network. / Bezazi, A.; Pierce, S.G.; Worden, K.; Harkati, E.
In: International Journal of Fatigue, Vol. 29, No. 4, 04.2007, p. 738-747.Research output: Contribution to journal › Article
TY - JOUR
T1 - Fatigue life prediction of sandwich composite materials under flexural tests using a Bayesian trained artificial neural network
AU - Bezazi, A.
AU - Pierce, S.G.
AU - Worden, K.
AU - Harkati, E.
PY - 2007/4
Y1 - 2007/4
N2 - 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.
AB - 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.
KW - sandwich composites
KW - fatigue
KW - neural networks
KW - lifetime prediction
U2 - 10.1016/j.ijfatigue.2006.06.013
DO - 10.1016/j.ijfatigue.2006.06.013
M3 - Article
VL - 29
SP - 738
EP - 747
JO - International Journal of Fatigue
JF - International Journal of Fatigue
SN - 0142-1123
IS - 4
ER -