eprintid: 773
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/07/73
datestamp: 2023-11-09 15:48:55
lastmod: 2023-11-09 15:48:55
status_changed: 2023-11-09 15:23:08
type: book
metadata_visibility: show
creators_name: Hidayat, M.I.P.
creators_name: Yusoff, P.S.M.M.
title: Optimizing neural network prediction of composite fatigue life under variable amplitude loading using bayesian regularization
ispublished: pub
note: cited By 3
abstract: Neural networks (NN) found its application in fatigue field, especially in fatigue life assessment of composite materials in recent years. The use of NN in the field of application also implies the necessity for optimizing the NN prediction of the composite fatigue life with respect to the presence of limited fatigue data available and fatigue condition of varying stress amplitudes. In the present chapter, optimizing NN prediction of fatigue life under variable amplitude loading (various stress ratio conditions) in relation with the availability of limited fatigue data for polymeric-based composites is presented. Multilayer perceptrons-based NN model is developed, and the training algorithm of Levenberg-Marquardt incorporating adaptive Bayesian regularization is used in the present study. From the simulation results obtained, it can be shown that training the developed network with fatigue data of only two stress ratios, which represent limited fatigue data, gave reasonably accurate fatigue life prediction under wide range of stress ratio values. The reliability and accuracy of the NN prediction were quantified by small mean square error (MSE) values. Finally, when using much less training fatigue data (22 from the total fatigue data), the network can still produce significant coefficient of determination between the prediction results and those obtained by the experiment. © 2010 by Taylor & Francis Group, LLC.
date: 2009
publisher: CRC Press
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045635912&partnerID=40&md5=3b1cd00c04db64206f4a785ede69d8c2
full_text_status: none
publication: Composite Materials Technology: Neural Network Applications
pagerange: 221-250
refereed: TRUE
isbn: 9781420093339; 9781420093322
citation:   Hidayat, M.I.P. and Yusoff, P.S.M.M.  (2009) Optimizing neural network prediction of composite fatigue life under variable amplitude loading using bayesian regularization.     CRC Press, pp. 221-250.  ISBN 9781420093339; 9781420093322