Morphological characteristics of stone aggregates, including particle shape, angularity, and surface texture, have a significant impact on the performance of hot-mix asphalt materials. To accurately identify and quantify these critical aggregate characteristics, well-defined particle descriptors are essential. Moreover, because a large number of irregular particles must be assessed to adequately characterize an aggregate material, descriptors that can be quantified with automated machines are preferred. In processing true three-dimensional (3-D) data from a laser scanner, wavelet-based 3-D particle descriptors are proposed as a way to characterize individual stone particles. Aided by the multiresolution analysis feature of the wavelet transform, these descriptors provide a generalized, comprehensive, and objective way of describing aggregates. This approach was implemented in conjunction with an automated laser-profiling device built for rapidly characterizing the size and shape properties of aggregate samples. Tests with this equipment have produced data that show strong correlations between the wavelet-based particle descriptors and visual perceptions of the aggregate morphological properties. These results demonstrate that the wavelet-based approach is a promising method for quantifying these important aggregate properties.
Abstract