Acquiring the size distributions of the aggregates using percolation modeling

Abstract

The recent computation-based image processing techniques the granular aggregates for attaining the size histograms could be a versatile and efficient alternative to the conventional sieve analysis tests, which demand laboratory equipments. Herein a new percolation-based framework is developed for acquiring such particle size distribution which pertains distinctive advantages relative to the recent techniques. Starting with the local binarization of the given cluster of aggregates, the connected chains have been identified by means of percolation and the internal aggregates have been discerned via minimizing the propagation flux and maximizing the roundness in global and local scales respectively. The method is verified via correlating with the experimental gradation, where the higher accuracy versus the conventional binarization is achieved and justified and the correlation with the corresponding resolution is addressed. The developed framework could be utilized as a fast and versatile method for determining the aggregate size distribution in any given granular medium, particularly those of containing irregular and random geometries pertaining sharp corners.