Evaluation of Aggregate Size and Shape by Means of Segmentation Techniques and Aggregate Image Processing Algorithms

Morphological properties of mineral aggregates are known to affect pavement and railroad track mechanistic behavior and performance significantly in relation to strength, modulus, and permanent deformation. With imaging technology, an objective and accurate measurement of aggregate particle size and shape properties can be obtained in a rapid, reliable, and automated fashion. But these advances in aggregate imaging must be brought to project sites and quarries in the field. This paper introduces field image-acquisition and image-processing techniques for extraction and analyses of size and shape properties of individual aggregate particles. Referred to as segmentation techniques, image-processing methods developed in this study analyzed two-dimensional field images of aggregates captured by a digital single-lens reflex camera. The segmented aggregate images were fed into the validated University of Illinois aggregate image analyzer to quantify particle size and shape properties by means of its image-processing algorithms for flat-and-elongated ratio, angularity index, and surface texture index. The developed method successfully determined the properties of coarse-aggregate samples collected from various depths in a layer of railroad track ballast. The promising preliminary results indicate that these segmentation techniques could be considered in the field for capturing several aggregate particles rapidly and reliably in a single image so that (a) size and shape properties of individual particles could be analyzed and (b) both spatial property variability and property changes with layer depth and usage (i.e., property degradation in time) could be evaluated under service loading.