Particle granulometry plays an important role in the engineering behavior of many sands. However, the evaluation of particle shape and size has historically been a tedious and labor-intensive process. The recent availability of dynamic image analysis (DIA) makes it possible to evaluate many particle shape and size parameters, quickly and conveniently. These shape parameters include sphericity, roundness, aspect ratio, circularity, and convexity; while size descriptors include the diameter of a circle of equal projection area (EQPC), a variety of Feret diameters, as well as inscribed and circumscribed circle diameters. The terms roundness and sphericity are commonly used to describe how close a particle resembles a sphere, with many definitions in common use. However, it is not immediately evident how these roundness descriptors correlate. The correlation of nine shape and six size descriptors was investigated for six sands that reflect the breadth of particle shapes and sizes that may be encountered. The analysis was based on 1,000 images of each sand obtained using two-dimensional DIA apparatus. The study demonstrates that there is no correlation between size and shape parameters, and that shape descriptors can be reduced to four independent shape parameters representing the granulometry of sand at different scales. The use of size and shape descriptors for classification of sand was explored using six machine learning algorithms including support vector machines (SVMs), random forest, decision tree, bagging tree, $k$ -nearest neighbors (KNN), and bagging KNN. Classification accuracies of 77% and 66% were achieved using size and shape features, respectively. The mean accuracy improved to 87% when combining both size and shape descriptors using bagging KNN and random forest classifiers. The analysis also revealed an important hierarchy of size and shape features employed, with EQPC and Wadell’s roundness alone classifying sands with 70% accuracy.

The morphology of two types of complex calcareous sand was investigated in this study. The materials were selected owing to their different geologic and biologic origins. Ledge Point is a bioclastic coastal sand, while Browse #1 is a hemipelagic sand. These two sands fall outside the range of common data sets used to correlate mechanical properties to particle shape parameters. Morphologic analysis of these calcareous sediments can aid with understanding the engineering behavior of calcareous soils. Moreover, sediments source tracing information could also be inferred from particle shape analysis. Two-dimensional Dynamic Image Analysis (DIA) was employed to capture five million and eight million particle images of each sand, respectively. A number of size parameters including diameter of equivalent projected circle (EQPC), Feret minimum, and Feret maximum diameter were efficiently obtained for each captured image using DIA, and used to investigate particle size distribution of these sediments. In addition, samples of over 800,000 particles were used to assess statistical distributions of various particle shape parameters including Aspect Ratio, Convexity, Sphericity, and Roundness-DIA (by volume). A Johnson family of distributions was found to provide a better fit to particle shape parameter distributions than the normal distribution for both sands. It is also shown that the Sphericity and Aspect Ratio are size independent, while Convexity and Roundness-DIA are correlated with particle size. Convexity is likely correlated with Sphericity for both calcareous sediments owing to their biogenic origin. Correlations of Roundness-DIA, Sphericity, Convexity, and Aspect Ratio and particle size are also observed but need more analysis.

This study investigates the efficacy of dynamic image analysis (DIA) for determining particle size and shape distribution. The method employs a high-frame-rate camera to image individual particles of sand that have been transported and separated using a stream of pressurized air. DIA can generate both particle size and shape information and provides a quantitative statistical description of the grain size and shape distribution within the specimen. The feasibility, repeatability, and accuracy of DIA for routine analysis of particle size and shape distribution was investigated using 16 granular soils spanning a number of common sizes and shapes. Several particle shape descriptors were evaluated, including aspect ratio, convexity, and sphericity. The effect of a variety of test parameters including moisture content, sample weight, primary air pressure, and test duration were explored to determine the optimal specimen weight and equipment settings for DIA. Finally, the efficacy of DIA in resolving mixtures of fine and coarse sands was also explored. The method proved to be feasible, repeatable, and accurate for providing particle size distributions spanning four orders of magnitude, in terms of particle size. DIA offers a number of advantages; the method is quick, requires small specimen sizes, and provides quantitative information on approximately 3–4 % of the particles in the specimen.

Two-dimensional (2D) dynamic image analysis (DIA) is gaining acceptance in geotechnical engineering research. Three-dimensional (3D) DIA extracts features from 8–12 projections of a particle and thus it is believed to verge on the true particle morphology. DIA is fast, efficient and convenient for characterising thousands of particles quickly; nevertheless, it captures shapes that are fundamentally different from the 3D morphologies reconstructed using micro-computed tomography (μCT). In DIA, particle features are interpreted using external images of a particle, which fail to account for differences in imaging perspectives. In addition, 2D and 3D shape descriptors are influenced by differences in dimensionality projection owing to variations in definition, dimensionality and perspectives of the particle images employed, which causes them to differ from their 3D counterparts. In this study, the sand particle size and shape descriptors obtained using both DIA and μCT are compared for three natural sands having wide granulometries. Three-dimensional DIA offers significant advantages in terms of efficiency, while providing adequate representation of Feret dimensions, sphericity and convexity. However, the study demonstrates that 3D roundness is difficult to characterise using DIA and that shape measurements of complex irregular calcareous sands obtained from 3D DIA are not comparable to those obtained using μCT.