Browse by author
Lookup NU author(s): Vasileios AngelidakisORCiD,
Dr Saimir Luli,
Dr Sadegh NadimiORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Kemp N, Angelidakis V, Luli S, Nadimi S
Publication type: Article
Publication status: Published
Journal: Journal of Imaging
Print publication date: 05/01/2022
Online publication date: 05/01/2022
Acceptance date: 16/12/2021
Date deposited: 10/01/2022
ISSN (electronic): 2313-433X
Altmetrics provided by Altmetric