Application of data mining techniques in predicting the behaviour of composite joints

This source preferred by John Vinney and Siamak Noroozi

Authors: Shrazi Kia, S., Noroozi, S., Carse, B. and Vinney, J.

Editors: Topping, B.H.V.

Pages: 47-48

Publisher: Civil-Comp Press

Place of Publication: Stirling

This data was imported from Scopus:

Authors: Kia, S.S., Noroozi, S., Carse, B. and Vinney, J.

Volume: 82

ISBN: 9781905088058

The evaluation and prediction of the failure probability and safety levels of composite components and structures is of extreme importance in structural design and manufacturing. A new application of data mining techniques for predicting the behaviour of pin-loaded composite joints is presented. The proposed system consists of combining data mining and soft computing techniques such as classification and clustering with fuzzy logic. By using these techniques, the relationship among different parameters such as edge distance and tensile strength of composite joints is modelled. A classification approach based on fuzzy clustering yielded the best predictive results. © Civil-Comp Press, 2005.

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