Triaxial compression behavior of sand and gravel using artificial neural networks (ANN)

Abstract The stress–strain and volume change behavior of sand and gravel under drained triaxial compression test conditions was modeled using feed-back artificial neural networks. A large experimental database obtained from published literature was used in training, testing, and prediction phases of three neural network based soil models. Issues related to the number of hidden units, magnitude of strain increment during feed-back, and over-training error are discussed. These models can accurately represent the effects of mineralogy, grain shape and size distribution, void ratio, and confining pressure. The observed behavior in terms of a non-linear stress–strain relation, compressive volume change at low stress levels, and volume expansion at high stress levels are captured well by these models.

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