Abstract:
Material that constitute aggregate depend on their packing efficiency that is defined by the shape distribution of
aggregates. Geometrical shape descriptors would not capture morphological aspects of different dimensional
scales and frequencies, failing to wholistically numerically represent shape in material prediction models. Fourier
shape descriptors are used to analyse single particle morphology, but efficiency to characterize shape of lump
samples has not been assessed. This study analyses aggregates milled for different number of revolutions (0, 200,
500 and 1000) in a Los Angeles Abrasion Value instrument, that contrives morphological alterations of different
scales. Means of zonal frequency components amplitudes (form, angularity and texture) are statistically different
for all classes with more than 99 % confidence. Support Vector Machine and K Nearest Neighbour algorithms
classified pairwise classification with an accuracy above 0.8 between milled and unmilled aggregates. Classification
of different degrees of milling had significantly lower accuracy (0.57 – 0.65). Mean of texture zone amplitudes
was dominant in feature importance in classifying milled and unmilled aggregates while mean of general
form zone amplitudes dominated in classification of different degrees of milling (200 – 1000). The amplitude of
the 45th frequency component is the dominant feature when all 53 frequency components are used as features, in
classifying milled and unmilled aggregates, while 4th to 6th frequency components are dominant in classifying
aggregates of different degree of milling.
