  A data-driven framework was used to predict the macroscopic mechanical
behavior of dense packings of polydisperse granular materials. The Discrete
Element Method, DEM, was used to generate 92,378 sphere packings that covered
many different kinds of particle size distributions, PSD, lying within 2
particle sizes. These packings were subjected to triaxial compression and the
corresponding stress-strain curves were fitted to Duncan-Chang hyperbolic
models. A multivariate statistical analysis was unsuccessful to relate the
model parameters with common geotechnical and statistical descriptors derived
from the PSD. In contrast, an artificial Neural Network (NN) scheme, trained
with a few hundred DEM simulations, was able to anticipate the value of the
model parameters for all these PSDs, with considerable accuracy. This was
achieved in spite of the presence of noise in the training data. The NN
revealed the existence of hidden correlations between PSD of granular materials
and their macroscopic mechanical behavior.
