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The application of neural network techniques to structural analysis by implementing an adaptive finite-element mesh generation

Published online by Cambridge University Press:  27 February 2009

Mansour Nasser Jadid  and
Daniel R. Fairbairn
Mansour Nasser Jadid
Affiliation:
Department of Building Technology, King Faisal University, Kingdom of, Saudi Arabia.
Daniel R. Fairbairn
Affiliation:
Department of Building Technology, King Faisal University, Kingdom of, Saudi Arabia. Department of Civil Engineering and Building Science, The University of Edinburgh, Edinburgh, U.K.
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Abstract

This study focuses on the application of neural network techniques to adaptive remeshing of an idealized squareshaped structure and individual triangle by using triangular elements. The backpropagation learning algorithm is implemented by a supervised training technique to deal with the problem of remeshing structural elements in a structural analysis. A recent study introduced a structural remeshing that incorporated a finite-element analysis with an adaptive mesh generation technique. The main objective of the study is to demonstrate how neural networks can be employed to remesh structural elements without using numerically intensive computations. One essential requirement of this approach is the selection of feasible and appropriate training and testing data. The exploration of neural network in remeshing structural elements is a fundamental technique that looks beyond the finite-element and adaptive mesh generation techniques. It also demonstrates the capability of neural network to represent n-dimensional space and track each individual characteristic in that separate space. In general, an overview is presented, and the potential of neural networks to use the backpropagation algorithm instead of the more conventional approach of numerical methods is demonstrated.

Keywords

Idealized Square-Shaped StructureIndividual Trianglen-dimensional Space
Type
Research Article
Information
AI EDAM , Volume 8 , Issue 3 , Summer 1994 , pp. 177 - 191
Copyright
Copyright © Cambridge University Press 1994

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