Abstract:
Aggregates are packed effectively packed in applications including pervious concrete, mechanical
performance of which, depends primarily on packing characteristics. Size and shape distribution of
aggregates affect packing. Several computational methods are used to represent shape of aggregates
numerically, from analyses of digital images, yet, they have not been compared. This study aims to
analyse representability of shape aspects of aggregates from different computational methods.
Crushed rock-aggregates were grouped into five clusters and milled in LAAV machine for different
number of revolutions (0–2000) to induce morphological changes, Samples were then sieved and
aggregates from 5 to 30 mm in diameter were obtained (7191 in total). Aggregates were painted in
black oil paint, laid on white sheets and digital images were obtained and analysed using an open
source software ImageJTM while shapes were defined based on 14 computational methods. Statistical
tests Pearson’s Correlations, Principal Components Analysis and K-means Cluster analysis were used to
assess shape factors. In conclusion, no shape factor singularly represented the morphological changes
on aggregate particles. A combination of shape factors is required. The data matrix had three
dimensions and three shape factors Circularity, Kumbrein solidity and Barksdale shape factor optimally
represented aggregate shape index.
