Artificial Neural Networks for Modeling Drained Monotonic Behavior of Rockfill Materials
Publication: International Journal of Geomechanics
Volume 14, Issue 3
Abstract
In this paper, the feasibility of developing and using artificial neural networks (ANNs) for modeling the monotonic behaviors of various angular and rounded rockfill materials is investigated. The database used for development of the ANN models is comprised of a series of 82 large-scale, drained triaxial tests. The deviator stress-volumetric strain versus axial strain behaviors were first simulated by using ANNs. A feedback model using multilayer perceptrons for predicting drained behavior of rockfill materials was developed in the MATLAB environment, and the optimal ANN architecture was obtained by a trial-and-error approach in accordance with error indexes and real data. Reasonable agreement between the simulated behaviors and the test results was observed, indicating that the ANNs are capable of capturing the behavior of rockfill materials. The ability of ANNs to predict the constitutive hardening-soil model parameters, residual deviator stresses, and corresponding volumetric strain was also investigated. Moreover, the generalization capability of ANNs was also used to check the effects of items not tested, such as dry density, grain-size distributions, and Los Angeles abrasion.
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Acknowledgments
The author is grateful to the Department of Geotechnical Engineering, Road, Housing and Urban Development Research Center (BHRC), for conducting the tests and for financial support, and to the Ministry of Energy, as the project’s client, for providing the data for this research. Also, the author thanks Dr. Piltan Tabatabei Shorjeh for his valuable suggestions. The open source code of the proposed models in the MATLAB7 environment is available from the author.
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Published In
International Journal of Geomechanics
Volume 14 • Issue 3 • June 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 22, 2012
Accepted: May 28, 2013
Published online: May 30, 2013
Published in print: Jun 1, 2014
Discussion open until: Aug 20, 2014
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