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

06/10/2020

∙

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.

READ FULL TEXT

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

Thermodynamics-based Artificial Neural Networks (TANN) for multiscale modeling of materials with inelastic microstructure

Viscoelastic Constitutive Artificial Neural Networks (vCANNs) - a framework for data-driven anisotropic nonlinear finite viscoelasticity

Tensor-Train Compression of Discrete Element Method Simulation Data

FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction

Learning Constitutive Relations using Symmetric Positive Definite Neural Networks

Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties

Sequential subspace optimization for recovering stored energy functions in hyperelastic materials from time-dependent data

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

Related Research

Thermodynamics-based Artificial Neural Networks (TANN) for multiscale modeling of materials with inelastic microstructure

Viscoelastic Constitutive Artificial Neural Networks (vCANNs) - a framework for data-driven anisotropic nonlinear finite viscoelasticity

Tensor-Train Compression of Discrete Element Method Simulation Data

FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction

Learning Constitutive Relations using Symmetric Positive Definite Neural Networks

Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties

Sequential subspace optimization for recovering stored energy functions in hyperelastic materials from time-dependent data