Determining Aggregate Grain Size Using Discrete-Element Models of Sieve Analysis

Abstract

The grain size of aggregate particles is crucial to the mixture gradation of discrete-element (DE) models when realistic aggregate shapes are simulated. The objective of this study was to answer the question of how to determine the grain size of aggregates using DE models based on virtual sieving analysis. First, virtual sieving analysis models were developed with prolate ellipsoid, oblate ellipsoid, and cubic-shaped particles, and virtual sieving was performed under three vibration patterns, namely, vertical, horizontal, and hybrid vibration. The influence and efficiency of the vibration patterns were analyzed based on the results of the virtual sieving analysis. Then, the virtual sieving analysis was conducted with realistic aggregate shapes. By analyzing the test results, the shape sieving factor (Ssf) was derived and was used to calculate the grain size of individual particles. For further validation, the grain size (Gs) of selected aggregates was measured by lab manual measurement and virtual sieving analysis, separately. Then the test results were analyzed and compared. The main findings from this study include the following: (1) vibration patterns had significant impacts on the results of the virtual sieving analysis, and vertical vibration is recommended for virtual sieving analysis; (2) particle shapes had important impacts on the results of the virtual sieving analysis, and it was determined that aggregates with cubic shapes are relatively difficult to pass through the sieve meshes; (3) most particles can pass through smaller sieve apertures than their equivalent-volume spheres; (4) the approach to virtual sieving analysis developed in this study was validated by lab sieving tests, and the shape sieving factor (Ssf) derived from the virtual sieving analysis can be used to generate DE models with more accurate gradation.