Modeling the Mechanical Behavior of Carbonate Sands Using Artificial Neural Networks and Support Vector Machines
Publication: International Journal of Geomechanics
Volume 16, Issue 1
Abstract
Carbonate sands that are specific soils have some unusual characteristics, such as particle crushability and compressibility, that distinguish their behavior from other types of soil. Because of their large diversity, they have a wide range of mechanical behavior. Recently, there have been many attempts to predict the mechanical behavior of carbonate sands, but all these attempts have been focused on experimental and case studies of some specific soils, and there is still no unique method that can consider all types of carbonate sands behavior and describe their various aspects. In the present study, two artificial intelligence-based models, namely artificial neural networks and support vector machines are used together and comparatively to predict the mechanical behavior of different carbonate sands. The models were trained and tested using a database that included results from a comprehensive set of triaxial tests on three carbonate sands. The predictions of the proposed models were compared with the experimental results. The comparison of the results indicates that the proposed approaches were accurate and reliable in representing the mechanical behavior of various carbonate sands.
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Published In
International Journal of Geomechanics
Volume 16 • Issue 1 • February 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Jul 23, 2014
Accepted: Feb 21, 2015
Published online: Jun 20, 2015
Discussion open until: Nov 20, 2015
Published in print: Feb 1, 2016
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