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Artificial Neural Networks for Modeling Drained Monotonic Behavior of Rockfill Materials
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.
Artificial Neural Networks for Modeling Drained Monotonic Behavior of Rockfill Materials
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.
Artificial Neural Networks for Modeling Drained Monotonic Behavior of Rockfill Materials
Araei, Ata Aghaei (author)
2013-05-30
Article (Journal)
Electronic Resource
Unknown
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