Artificial Neural Networks for Modeling Shear Modulus and Damping Behavior of Gravelly Materials
Publication: International Journal of Geomechanics
Volume 17, Issue 2
Abstract
In this paper, the feasibility of developing and using artificial neural networks (ANNs) for modeling the shear modulus (G) and damping ratio (D) versus shear strain (γ) behaviors of dry and saturated isotropic and anisotropic highly compacted gravelly materials is investigated. The database used for development of the ANN models comprises a series of 172 large-scale dynamic triaxial tests on 12 different materials. The cyclic triaxial tests were carried out under different confining pressures and loading frequencies. A feed-forward model using multilayer perceptrons (MLPs) for predicting behavior of gravelly materials was developed, and the optimal ANN architectures were obtained by a trial-and-error approach in accordance with error indices and real data. The ability of ANNs to predict the confining pressure, loading frequency, anisotropy, and dry and saturation effects on G-γ and D-γ behaviors was investigated. Reasonable agreements between the simulated and test results were observed, indicating that the ANNs are capable of capturing the dynamic behavior of gravelly materials. Moreover, the generalization capability of the ANNs was also used to check the effects of items not tested, such as confining pressure, loading frequency, dry density, and grain-size distribution.
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Acknowledgments
The authors are 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 of Iran as the project client for providing data for this research. Open-source code of the proposed models in the MATLAB 7 environment is available from the authors.
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Published In
International Journal of Geomechanics
Volume 17 • Issue 2 • February 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Dec 30, 2014
Accepted: Jan 22, 2016
Published online: Jul 22, 2016
Discussion open until: Dec 22, 2016
Published in print: Feb 1, 2017
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