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 language | English (US) |
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Title of host publication | Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference |
Pages | 4869-4883 |
Number of pages | 15 |
Volume | 7 |
State | Published - 2005 |
Event | 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Austin, TX, United States Duration: Apr 18 2005 → Apr 21 2005 |
Other
Other | 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference |
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Country/Territory | United States |
City | Austin, TX |
Period | 4/18/05 → 4/21/05 |
ASJC Scopus subject areas
- Architecture