Application of artificial neural networks to predict the particle-scale contact force of photoelastic disks

Abstract

Measuring the contact force of photoelastic granular materials in a two-dimensional system either has a high dependence on the experimental parameters or requires expensive computations. This study proposes a supervised machine learning strategy that constructs a universal relation between the features of a stressed photoelastic disk and the corresponding total contact force acting on it via an artificial neural network (ANN). Most of the common circumstances of particle–particle contact that occurs in real photoelastic granular flow experiments are considered in the data. The ANN reached a test R-squared value of about 0.96. Therefore, the proposed machine learning framework is an efficient and accurate force measurement technique for two-dimensional photoelastic granular flows.