Neural Network Modeling of Anisotropic Aggregate Behavior from Repeated Load Triaxial Tests

Abstract

Determining horizontal specimen response in a repeated load triaxial test is essential to properly characterize the directional dependency of unbound aggregate resilient behavior under anisotropic loading conditions. Recent research has applied artificial neural networks (ANNs) for predicting, in the absence of lateral deformation data, the anisotropic stiffness properties of granular materials from standard AASHTO tests. Feed-forward backpropagation-type neural networks were successfully trained with two triaxial stresses (confining pressure and applied deviator stress), measured vertical deformation, and two aggregate properties (compacted dry density and crushed particle percentage) used as input variables. The output variables were the horizontal and shear moduli for which the actual (target) values were derived and computed from test results. The ANN models predicted the two moduli, with mean errors of less than 3 percent compared with those computed by using experimental stresses and strains. Both the applied stress state and the aggregate properties were found to affect the generalization and thus the prediction ability of the ANN models.