Use of Machine Learning for unraveling hidden correlations between Particle Size Distributions and the Mechanical Behavior of Granular Materials

TL;DR

This study employs machine learning, specifically neural networks, to uncover hidden correlations between particle size distributions and the mechanical behavior of granular materials, using extensive DEM simulations and statistical analysis.

Contribution

It introduces a data-driven neural network approach that predicts mechanical behavior from PSDs, revealing hidden correlations not identified by traditional statistical methods.

Findings

Neural network accurately predicts stress-strain model parameters.

Hidden correlations between PSD and mechanical behavior were discovered.

DEM simulations provided extensive data for training and validation.

Abstract

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.