A microstructure-based finite element analysis of the response of sand

This paper presents a novel contribution towards understanding the stress distribution amongst the constituent grains of an intact sand under loading. Photoelasticity using birefringent materials has shown that forces in granular media are transmitted from particle-to-particle via their contacts and the mode of load propagation forms a complex force network. Particles carrying above average load appear to form a network with special characteristics where stronger forces are carried through chain-like particle groups, often referred as force chains. Fonseca et al. (2013) showed that for a sand under shearing, the contact normals tend to be orientated along the direction of the major principal stress, which suggests the formation of force chains. Moreover, these quasi-vertically oriented vectors were shown to be associated with contacts having large surface areas, contributing to the formation of solid columnar structures of stress transmitting grains. This early study demonstrates that a full characterization of force chains for real soils requires accounting for the effects of the soil microstructure, including grain morphology and contact topology, which the idealized nature of the particles used for discrete element method simulations and photoelasticity studies cannot capture. In the present work, high resolution x-ray tomographic data of an intact sand is converted into a two dimensional finite element mesh, so that the microstructural details, such as the geometrical arrangement of the grains and pores, as well as grain shape and contact topology are incorporated in the model. In other words, the soil microstructure is modelled using a computation approach that considers all available geometrical data. The results suggested that the ability of the grains to transmit stress via their contacts is directly associated to the degrees of freedom they have to move and rearrange, which in turn is controlled by the topology of the contacts. The insights into the effects of microstructure on the stress transmission mechanisms provided in this study are fundamental to better understand and predict the macro scale response of soil

Image-based modelling of shelly carbonate sand for foundation design of offshore structures

For the most part, carbonate soils are of biogenic origin comprising skeleton bodies and shells of small organisms, the shelly carbonate sands. Owing to the complex microstructure of these soils, there are many uncertainties related to their mechanical behavior, in particular, regarding their high compressibility. Aside from obvious safety concerns, the inability to predict the behavior of carbonate sands involves extensive remedial measures and leads invariably to severe time delays and increased construction costs. This study makes use of 3D images of the internal structure of a shelly carbonate sand under compression on a small oedometer placed inside an x-ray scanner. The images are first used to gain insights into the grain-scale properties of the material and then the soil microstructure is virtualized and simulated within a framework of combined discrete–finite-element method. This study contributes towards a better understand the grain-scale phenomena shaping the macro response of shelly carbonate sands, which differs considerably from more commonly studied silica sands of terrigeneous origin

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 (\ub5CT) 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

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition

Head & Neck Oncology: purpose, scope and goals-charting the future

For many years now there has been a growing frustration with the statistics of head and neck cancer. Despite the many advances in diagnosis and therapy, there has been little change in the prognosis for most cancers of the head and neck in the last 50 years, so what is the point of yet another journal? Well, it is not all bad news

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep