Prediction of Packing Density of Multi-Sized Aggregates using Grading Models and Compressible Packing Model

The packing density and stability of the aggregate mixtures significantly affect the quality of concrete, particularly in infrastructural applications. The Carneiro and Dinger-Funk (D-F) models are two of the tools utilized in the generation of aggregate grading for concrete applications. For the first time, this study aimed to develop a methodology for the determination of the Carneiro distribution modulus (Pr) and the D-F distribution modulus (q) for a given aggregate system that provides optimal packing characteristics considering the packing density and segregation potential. In this study, combinations of two coarse aggregate classes (19 to 4.75 mm and 9.5 to 2.36 mm) and two fine aggregates (4.75 to 0.15 mm and 600 to 75 μm) were generated using a number of trial values of Pr and q. The packing density and segregation potential of each aggregate mixture were then computed using the compressible packing model, considering equivalent idealized spherical aggregate particles. For the aggregates considered in this study, results indicated that the optimal mixtures result in the range 0.85 ≤ Pr ≤ 0.90 and 0.15 ≤ q ≤ 0.25 for Carneiro and D-F models, respectively. Although both models appear to produce equivalent aggregate mixture characteristics, the D-F model is less responsive to changes in the distribution modulus. Hence, it is more flexible for obtaining an aggregate mixture composition with optimal packing characteristics