Transforming Results from Model to Prototype of Concrete Gravity Dams Using Neural Networks
Publication: Journal of Engineering Mechanics
Volume 137, Issue 7
Abstract
A new method using neural networks for the transformation of results from dam models to prototypes has been proposed and validated through application to Koyna and Pine-Flat Dams, which have also been investigated by other researchers. The neural network has been called the neurotransformer. The common method for building a suitable experimental model for a dam to be tested on a shaking table is linear dimensional analysis or simply linear scaling (LS). However, because LS is theoretically applicable to linear systems, it generally provides imprecise results of transformation for extreme loading when the model or the prototype experiences noticeable nonlinearity. In this paper, it is shown through numerical simulation of the dynamic behaviour of Koyna Dam and its model under strong earthquakes, which cause nonlinear behavior in both the dam and its model, that transformation by neural networks is considerably more precise than LS. To show the method can also be applied to other dams, the same procedure was successfully applied to Pine-Flat Dam; again, the neurotransformer outperformed the LS.
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Acknowledgments
The writers would like to thank the deputy of higher education and deputy of research of Sharif University of Technology for partially supporting this research.
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Information & Authors
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Published In
Journal of Engineering Mechanics
Volume 137 • Issue 7 • July 2011
Pages: 484 - 496
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Mar 17, 2010
Accepted: Dec 20, 2010
Published online: Dec 22, 2010
Published in print: Jul 1, 2011
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