TITLE

Crushing Prediction of Axially Loaded Rectangular Composite Tubes Using Artificial Neural Networks

AUTHOR(S)
El Kadi, Hany
PUB. DATE
November 2010
SOURCE
International Review of Mechanical Engineering;Nov2010, Vol. 4 Issue 7, p952
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
In this work, the artificial neural networks (ANN) technique is used to predict the crushing behavior of axially-loaded glass fiber/epoxy composite rectangular tubes. Predictions are compared to experimental results and are shown to be in good agreement. Effects of parameters such as network architecture and number of hidden layer neurons are also considered. The study shows that ANN techniques can effectively be used to predict the crushing response characteristics of rectangular composite tubes with various aspect ratios subjected to axial loading.
ACCESSION #
77231097

 

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