Micro computed tomography images of capillary actions in rough and irregular granular materials.

The present work investigates the effect of both surface roughness and particle morphology on the retention behaviour of granular materials via X-ray micro-computed tomography (µCT) observations. X-ray µCT images were taken on two types of spherical glass beads (i.e. smooth and rough) and two different sands (i.e. natural and roughened). Each sample was subjected to drainage and soaking paths consisting in a multiphase 'static' flow of potassium iodine (KI) brine (wetting phase) and dry air (non-wetting phase). Tomograms were obtained at different saturation states ranging from fully brine saturated to air dry conditions with 6.2 µm voxel size resolution. The data acquisition and pre-processing are here described while all data, a total of 48 tomograms, are made publicly available. The combined dataset offers new opportunities to study the influence of surface roughness and particle morphology on capillary actions as well as supporting validation of pore-scale models of multiphase flow in granular materials.

Characterisation of physical and mechanical properties of seven particulate materials proposed as traction enhancers.

Particulate materials are utilised in many applications to manipulate the friction between surfaces. This dataset provides the characteristics of particulates used as rail sand in the train's wheel/rail interface (via an on-board system) to facilitate the train's acceleration and deceleration. Seven materials are studied including Austrian rail sand, standard Great British rail sand, waste glass beads, recycled crushed glass, non-coated alumina, coated alumina, and dolomite. The main objective of this research is to provide a physical and mechanical characterisation of these granular materials in terms of their density, bulk behaviour, particle size, particle shape, hardness, reduced modulus, and mineralogical properties. In particular, three-dimensional raw and post-processed micro-computed tomography images of more than 1200 particles are shared. The results provide a detailed dataset which can be used in ongoing and future experimental and numerical investigations studying the role of particulates in the wheel/rail interface.

Kinetic adhesion test to determine particle surface energy.

A new hardware is described to quantify the particle surface energy by assuming that the Johnson Kendall and Roberts theory of elastic-adhesive contacts is applicable. The setup is used in the active section of the measurement, where newly designed elements provide the sharp impact needed to detach the particles under the action of their own kinetic energy. It employs a selection of sensors to provide the necessary measurements in a streamlined procedure, which lets the user complete one test in less than one minute. The temporal resolution is 1µs for the contact time measurement and the velocity has a repeatability of 1%. The surface energy is a significant parameter for the characterisation of particulate materials and is widely used in Discrete Element simulations of the bulk behaviour.

How Do Roots Interact with Layered Soils?

Vegetation alters soil fabric by providing biological reinforcement and enhancing the overall mechanical behaviour of slopes, thereby controlling shallow mass movement. To predict the behaviour of vegetated slopes, parameters representing the root system structure, such as root distribution, length, orientation and diameter, should be considered in slope stability models. This study quantifies the relationship between soil physical characteristics and root growth, giving special emphasis on (1) how roots influence the physical architecture of the surrounding soil structure and (2) how soil structure influences the root growth. A systematic experimental study is carried out using high-resolution X-ray micro-computed tomography (µCT) to observe the root behaviour in layered soil. In total, 2 samples are scanned over 15 days, enabling the acquisition of 10 sets of images. A machine learning algorithm for image segmentation is trained to act at 3 different training percentages, resulting in the processing of 30 sets of images, with the outcomes prompting a discussion on the size of the training data set. An automated in-house image processing algorithm is employed to quantify the void ratio and root volume ratio. This script enables post processing and image analysis of all 30 cases within few hours. This work investigates the effect of stratigraphy on root growth, along with the effect of image-segmentation parameters on soil constitutive properties.