A porous model to simulate the evolution of the soil–water characteristic curve with volumetric strains

Arroyo, Hiram; Rojas, Eduardo; Pérez-Rea, María de la Luz; Horta, Jaime; Arroyo, José Manuel Fernández

doi:10.1016/j.crme.2015.02.001

Cited by 14 publications

(12 citation statements)

References 23 publications

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“…In recent years, various attempts have been made to predict SWCC from the pore size distribution (PSD) [1][2][3][4]. For a given state of soils, it has been reported that PSD has great influence on SWCC [5,6].…”

Section: Introductionmentioning

confidence: 99%

Determining Soil-Water Characteristic Curves from Mercury Intrusion Porosimeter Test Data Using Fractal Theory

et al. 2019

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Accurate determination of soil-water characteristic curve (SWCC) is of immense importance for understanding the mechanical behavior of unsaturated soils. Due to the difficulty and long duration of experimental procedures, it is of great significance to estimate the SWCC by indirect methods. To address this issue, in this article an effective fractal method is proposed for predicting the SWCC based on mercury intrusion porosimeter (MIP) data. Only two characteristic parameters, namely the fractal dimension and air-entry value, are needed in the presented approach. Detailed procedures for determining the parameters are clearly elaborated. Due to the influence of sample size difference on the equivalent connected pore size, a sample scale effect coefficient is proposed to predict air-entry values. The concept of “critical pore size” is introduced to obtain the optimal fractal dimension, which can accurately reflect the fractal behaviour of SWCC samples. By comparisons between predicted and experimental SWCCs, the validation of the proposed method is verified. The comparisons reveal the good agreement between the proposed approach and laboratory experiments.

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Section: Introductionmentioning

confidence: 99%

Determining Soil-Water Characteristic Curves from Mercury Intrusion Porosimeter Test Data Using Fractal Theory

et al. 2019

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“…The parameters contained within the parentheses in Equation (2) depend on the distribution of water inside the soil; the way in which their value is computed is described elsewhere [12,13], for the sake of completeness we will roughly describe them herein.…”

Section: The Solid-porous Modelmentioning

confidence: 99%

An effective stress approach for hydro-mechanical coupling of unsaturated soils

2016

Self Cite

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The simulation of the mechanical and hydraulic behaviour of soils is one of the most important tasks in soil mechanics. It is inaccurate to consider that the behaviour of saturated and unsaturated soils as if their governing laws were utterly different, this last condition is not sufficient to do so. However, unlike the laws governing the behaviour of saturated soils, those used to describe the behaviour of unsaturated soils lack the simplicity and predictability associated to the complexity of the phenomena that occur within these porous media. This is why it is important to establish a unified soil mechanics theory to reconcile saturated and unsaturated soil mechanics. In the present work, we describe a simple analytical equation to obtain effective stresses for any type of soil. The equation is coupled to an elastoplastic constitutive model which is capable to reproduce the stress-strain relationship of soil taking into account the hydro-dynamic coupling.

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“…These models focus on real physical phenomena that occur at the level of soil pores and that together with soil structure influence unsaturated soil hydrological properties [Mahmoodlu et al, 2016]. Real representations of pore spaces obtained from computed microtomography scanning (mCT) [Ahrenholz et al, 2008] or equivalent pore network models [Joekar-Niasar et al, 2013;Arroyo et al, 2015;Rostami et al, 2015] and individual phenomena leading to the hysteresis effect such as contact angle hysteresis [Zhou, 2013] or soil-water interactions and pore morphology [Chan and Govindaraju, 2011;Gan et al, 2013] are used and analyzed in these models. While these models provide insight into various physical factors leading to hysteresis, their practical applications are very limited since they are very complicated and often dependent on hard-to-get input data.…”

Section: Introductionmentioning

confidence: 99%

An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method

Lamorski

Šimůnek

Sławiński

et al. 2017

Water Resources Research

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In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well‐known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE = 0.018 m3/m3) that are significantly lower than those for the M77 (RMSE = 0.025 m3/m3) or KP87 (RMSE = 0. 047 m3/m3) models.

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A porous model to simulate the evolution of the soil–water characteristic curve with volumetric strains

Cited by 14 publications

References 23 publications

Determining Soil-Water Characteristic Curves from Mercury Intrusion Porosimeter Test Data Using Fractal Theory

Determining Soil-Water Characteristic Curves from Mercury Intrusion Porosimeter Test Data Using Fractal Theory

An effective stress approach for hydro-mechanical coupling of unsaturated soils

An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method

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