Using the Artificial Neural Networks Methodology to Predict the Vertical Swelling Percentage of Expansive Clays
Publication: Journal of Materials in Civil Engineering
Volume 26, Issue 6
Abstract
Regular use of artificial neural networks (ANN) analysis for predicting the vertical swelling percentage of expansive clays may lead to inappropriate results in terms of their geophysical behavior. This paper presents two new ANN Models derived from a two-stage procedure. The models were estimated using the same data set from the previous paper, and their statistical fit was clearly found to be superior in comparison to the previous models. Furthermore, one of these two models exhibited the expected geophysical behavior. As this new ANN Model yields higher predicted swelling-percentage values, it can definitely be regarded as a preferable one in the sense of enlarging the safety margin in heave calculations.
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Acknowledgments
The paper is based on engineering studies conducted for various Israeli governmental departments and authorities, and thanks are therefore due them. The helpful assistance provided by Yacoub Najjar, of the Department Chair and Professor at University of Mississippi, contributed to the development of the paper, and thanks are therefore due him.
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Information & Authors
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Published In
Journal of Materials in Civil Engineering
Volume 26 • Issue 6 • June 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Feb 15, 2013
Accepted: Aug 26, 2013
Published online: Aug 28, 2013
Published in print: Jun 1, 2014
Discussion open until: Aug 17, 2014
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