Estimation of Aeroelastic Parameters of Bridge Decks Using Neural Networks
Publication: Journal of Engineering Mechanics
Volume 130, Issue 11
Abstract
A new method of estimating flutter derivatives using artificial neural networks is proposed. Unlike other computational fluid dynamics based numerical analyses, the proposed method estimates flutter derivatives utilizing previously measured experimental data. One of the advantages of the neural networks approach is that they can approximate a function of many dimensions. An efficient method has been developed to quantify the geometry of deck sections for neural network input. The output of the neural network is flutter derivatives. The flutter derivatives estimation network, which has been trained by the proposed methodology, is tested both for training sets and novel testing sets. The network shows reasonable performance for the novel sets, as well as outstanding performance for the training sets. Two variations of the proposed network are also presented, along with their estimation capability. The paper shows the potential of applying neural networks to wind force approximations.
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 130 • Issue 11 • November 2004
Pages: 1356 - 1364
Copyright
Copyright © 2004 ASCE.
History
Published online: Oct 15, 2004
Published in print: Nov 2004
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