This dataset has (i) 3D geometry files of mineral particles from Florida and Virginia and (ii) analyzed 3D particle geometries. A total of 382 particle geometry files are included in this dataset. The Florida particles in this dataset are limestones, while the Virginia particles are freshly crushed granites from a quarry in Richmond, Virginia. The geometry files are obtained by 3D structured light scanning and presented in wavefront .obj format. These files can be viewed using free 3D ge...

This folder contains 48 images of grains and the corresponding segmentation masks that form the majority of the images that were used to train the U-Net model that the 'Segmenteverygrain' Python package is based on. The images have filenames that terminate in '_image.png'; the mask filenames terminate in '_mask.png'. The mask rasters only contain three values: 0 for background, 1 for the grain itself, and 2 for the grain boundary.

This dataset consists of two types of image data for sand particle analysis, covering a variety of typical sand types. It is suitable for tasks such as image super-resolution, object detection, and instance segmentation. Sand Particle Super-Resolution Dataset contains 800 high-resolution images (4480×4480 pixels) captured using a Canon 5D Mark IV camera equipped with an EF 100 mm f/2.8 L IS USM macro lens. The dataset includes 8 types of sand particles: Fujian sand, Hebei Sand No.1, Heb...

This dataset is designed to support the automatic recognition and segmentation of nonoverlapping and overlapping particles in granular images. All images were captured from a top-down view using a Canon 5D Mark IV camera equipped with an EF24-105mm lens, with a resolution of 6720×4480 pixels. The dataset contains a total of 420 images. Each image has been annotated at the instance level using the LabelMe tool, with particles categorized into two types of labels: “nonoverlapping particles...

The datasets (84 flume, 118 field photos) cover a wide range of fluvial environments and include a variety of field site and flume experiment images. The datasets were grouped into three subsets according to sediment and channel conditions: (1) flume channel (84 photos; photo size ~ 0.2 m × 0.2m); (2) forested mountain rivers (70 photos; photo size ~ 1m × 1m); and (3) sparsely vegetated large rivers (6 photos; photo size ~ 20m × 20m). To train the machine learning model to better disting...

Supplementary Data for the paper "Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders" by E.J. Garboczi and N. Hrabe.

All the training data of triaxial testing is provided by discrete element modelling wherein a total of 4037 spherical particles with their radii uniformly distributed between 2mm and 4mm are used to generate the specimens. These specimens are isotropically consolidated to a confining pressure of 200 kPa. The normal and tangential contact stiffnesses are 105 N/m and 5 × 104 N/m, respectively. The interparticle frictional coefficient is 0.5, the particle density is 2600 kg/m3, the local ...

45,000 implicit DEM simulations for particle crushing, encompassing a total of 900 different particle types. This includes the Cartesian product of 20 distinct particle diameters, 15 different scaled shapes along the (X, Y, Z) axes, and 3 different compression axes under one-dimensional compression.

The present work investigates the effect of both surface roughness and particle morphology on the retention behaviour of granular materials. To study this, X-ray micro-computed tomography tests were performed on two different sands (i.e. natural and roughened). Each sample was subjected to either drainage or soaking paths consisting in a multiphase ‘static’ flow of potassium iodine (KI) brine (wetting phase) and dry air (non-wetting phase). Tomograms were taken at different saturation st...

3D digital sand grains