Quantification of Coarse Aggregate Angularity Based on Image Analysis

Abstract

Aggregate angularity affects the shear strength properties of asphalt concrete and granular base layers in pavements. It also improves aggregate interlock and load transfer properties of jointed concrete pavements. The development of a quantifiable index based on image analysis to characterize the angularity of coarse aggregate used in pavement layers is described. The new angularity index (AI) was developed as part of the University of Illinois Aggregate Image Analyzer, and the procedure was calibrated for two aggregate samples, rounded gravel and crushed stone, which possess the two extremes of particle angularity. A statistical study demonstrated that the AI computation technique is not only able to distinguish crushed stone from gravel but also is robust enough to give similar AI values regardless of the particle size and orientation. Furthermore, the crushed stone and gravel samples together with a 50–50 blend of the two samples by weight were tested for shear strength under triaxial loading conditions. The AI value computed for these samples could be correlated to the angle of internal friction and thus the shear strength properties of the samples. The AI distributions of representative aggregate samples from Illinois, crushed gravel, limestone, and dolomite, were also computed. The newly developed index has also demonstrated the capability to distinguish between crushed stone and crushed gravel samples. With this AI value as a measure of aggregate angularity, pavement engineers can objectively quantify the influence of aggregate angularity on asphalt concrete, portland cement concrete, and granular mix performance and thereby establish meaningful criteria relating aggregate properties to performance indicators.