Please use this identifier to cite or link to this item: https://doi.org/10.3390/en11061419
Title: A simple fractal-based model for soil-water characteristic curves incorporating effects of initial void ratios
Authors: Tao, G
Chen, Y
Kong, L
Xiao, H
Chen, Q 
Xia, Y
Keywords: Forecasting
Soil moisture
Soil structure interactions
Soil testing
Fractal dimensions (d)
Fractal model
Initial void ratios
Model parameters
Model prediction
Physical meanings
Soil-water characteristic curve
The soil-water characteristic curves (SWCC)
Fractal dimension
Issue Date: 2018
Publisher: MDPI AG
Citation: Tao, G, Chen, Y, Kong, L, Xiao, H, Chen, Q, Xia, Y (2018). A simple fractal-based model for soil-water characteristic curves incorporating effects of initial void ratios. Energies 11 (6) : 1419. ScholarBank@NUS Repository. https://doi.org/10.3390/en11061419
Rights: Attribution 4.0 International
Abstract: In this paper, a simple and efficient fractal-based approach is presented for capturing the effects of initial void ratio on the soil-water characteristic curve (SWCC) in a deformable unsaturated soil. In terms of testing results, the SWCCs (expressed by gravimetric water content) of the unsaturated soils at different initial void ratios were found to be mainly controlled by the air-entry value (?a), while the fractal dimension (D) could be assumed to be constant. As a result, in contrast to the complexity of existing models, a simple and efficient model with only two parameters (i.e., D and ?a) was established for predicting the SWCC considering the effects of initial void ratio. The procedure for determining the model parameters with clear physical meaning were then elaborated. The applicability and accuracy of the proposed model were well demonstrated by comparing its predictions with four sets of independent experimental data from the tests conducted in current work, as well as the literature on a wide range of soils, including Wuhan Clay, Hefei and Guangxi expansive soil, Saskatchewan silt, and loess. Good agreements were obtained between the experimental data and the model predictions in all of the cases considered. © 2018 by the authors.
Source Title: Energies
URI: https://scholarbank.nus.edu.sg/handle/10635/178532
ISSN: 1996-1073
DOI: 10.3390/en11061419
Rights: Attribution 4.0 International
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