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An analytical model to predict water retention curves for granular materials using the grain-size distribution curve Cover

An analytical model to predict water retention curves for granular materials using the grain-size distribution curve

Studia Geotechnica et Mechanica
Volume 44 (2022): Issue 4 (December 2022)
By: Linda Bouacida,  Sadok Feia,  Sid Ali Denine and  Noureddine Della  
Open Access
|Dec 2022
Figures & tables

Figures & Tables

Figure 1

Conceptual diagram showing the effect of (a) median particle size of uniform sand and (b) width of particle size distribution, on the shape of the soil-water characteristic curve (SWCC) of sand (Craig H. Benson et al. [14]).

Figure 2

Conceptual diagram presenting the effect of (a) the median particle size of uniform sand, and (b) the breadth of particle size distribution, on the parameters α and n (Craig H. Benson et al. [12]).

Figure 3

Typical soil water retention curve (Toll [59]).

Figure 4

Explanatory diagram of the drying and wetting processes in the porous network that is composed of cylinders with radius r; rm is the meniscus radius at the air-water interface (Do. [19]).

Figure 5

Schematic representation of the tensiometric method for the measurement of suction (Feia et al. [25]).

Figure 6

Experimental results used in this study (Feia et al. [25]).

Figure 7

Variation of the degree of residual saturation as a function of the density index.

Figure 8

Calibration of the model on the basis of the experimental data for a) Sand NEI-1, b) Sand NEI-2, and c) Sand NEI-3

Figure 9

Evolution of the parameter α as a function of the density index ID.

Figure 10

Evolution of the parameter (n) as a function of the density index ID.

Figure 11

Validation of the model through the simulation of a test, with a density index ID=0.9 and uniformity coefficient Cu=1.6.

Photo 1

Four types of sand.

Figure 12

Grain-size distributions of all four sands obtained by sieve analysis.

Figure 13

Water retention curves for all four types of sand.

Figure 14

Water retention curves for type 3 sand for different density index values.

Figure 15

Pore-access size distributions for all four types of sand.

Figure 16

Effect of sand density index on pore-access size distribution.

Figure 17

Comparison between the pore-access sizes of four types of sand.

Figure 18

Comparison between the results obtained by the proposed model and those calculated by the law of Della and Feia [47].

Values of the parameters of the proposed model for the three types of sand_

SandNEI-1ID= 0.9NEI-2ID= 0.7NEI-3ID= 0.5
Model parameters
α4.53.43
n8.57.36

Characteristics of the materials to be analyzed_

SandD50 (mm)Cueminemaxρs(g/cm3)
Type 10.181.50.510.792.65
Type 20.372.850.470.752.65
Type 30.422.470.470.762.65
Type 40.550.440.772.65

Characteristics of the used sands_

Sanddg50(μm)Cueminemaxρs(t/m3)
NE342061.50.5570.8842.65
Type of sand NEI-1NEI-2NEI-3
Density index ID 0.90.70.5
DOI: https://doi.org/10.2478/sgem-2022-0025 | Journal eISSN: 2083-831X | Journal ISSN: 0137-6365
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Language: English
Page range: 354 - 369
Submitted on: Jan 1, 2022
Accepted on: Sep 27, 2022
Published on: Dec 10, 2022
Published by: Wroclaw University of Science and Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
sand,
mean grain size,
mean pore size,
density index,
model,
pore size distribution,
water retention curve
Related subjects:
Geosciences,
Geosciences, other,
Materials sciences,
Composites,
Porous materials,
Physics,
Mechanics and fluid dynamics

© 2022 Linda Bouacida, Sadok Feia, Sid Ali Denine, Noureddine Della, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 44 (2022): Issue 4 (December 2022)
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