Multiple and generalized delamination detections using neural network technique and genetic algorithm

Hieu Le, H. P. Chen, Aditi Chattopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The delamination detection problem is formulated as an optimization problem with mixed type design variables using genetic algorithms. A recently developed finite element model based on an improved layerwise composite laminate theory is employed to calculate the natural frequencies of laminates with given delamination patterns. Artificial backpropagation neural networks are trained to simulate results from the finite element analysis. These artificial neural networks are chosen as function approximations, which are used to predict natural frequencies of delaminated laminates with satisfactory accuracy. Two different types of delamination detections - multiple through-the-width delamination and generalized delamination are considered in the present studies. Results with satisfactory accuracy have been obtained in detecting both multiple through-the-width delamination and generalized delamination using this technique.

Original languageEnglish (US)
Title of host publicationCollection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
Pages4869-4883
Number of pages15
Volume7
StatePublished - 2005
Event46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Austin, TX, United States
Duration: Apr 18 2005Apr 21 2005

Other

Other46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
CountryUnited States
CityAustin, TX
Period4/18/054/21/05

ASJC Scopus subject areas

  • Architecture

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    Le, H., Chen, H. P., & Chattopadhyay, A. (2005). Multiple and generalized delamination detections using neural network technique and genetic algorithm. In Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference (Vol. 7, pp. 4869-4883)