Parameterization of soil freezing characteristic curve for unsaturated soils

Teng, Jidong; Kou, Jingyuan; Yan, Xiaodong; Zhang, Sheng; Sheng, Daichao

doi:10.1016/j.coldregions.2019.102928

Cited by 67 publications

(47 citation statements)

References 37 publications

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“…When parameterizing the soil freezing function, often a residual or minimum water content limit is set (McKenzie et al, 2007; Shojae Ghias et al, 2018) that prevents the soil from freezing completely. Readers are directed to Watanabe and Osada (2016), Watanabe and Osada (2017), and Teng, Kou, Yan, Zhang, and Sheng (2020) for examples of laboratory measurements of different soils as well as Kurylyk and Watanabe (2013) and Amiri, Craig, and Kurylyk (2018) for a more detailed description of soil freezing curves. The Permeability Function determines the permeability based on the liquid water saturation. As the ice saturation increases (i.e., during freezing) and/or when the water saturation decreases due to draining, the permeability will decrease by up to several orders of magnitude.…”

Section: Governing Equations and Characteristic Functionsmentioning

confidence: 99%

Guidelines for cold‐regions groundwater numerical modeling

et al. 2020

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The impacts of ongoing climate warming on cold-regions hydrogeology and groundwater resources have created a need to develop groundwater models adapted to these environments. Although permafrost is considered relatively impermeable to groundwater flow, permafrost thaw may result in potential increases in surface water infiltration, groundwater recharge, and hydrogeologic connectivity that can impact northern water resources. To account for these feedbacks, groundwater models that include the dynamic effects of freezing and thawing on ground properties and thermal regimes have been recently developed. However, these models are more complex than traditional hydrogeology numerical models due to the inclusion of nonlinear freeze-thaw processes and complex thermal boundary conditions. As such, their use to date has been limited to a small community of modeling experts. This article aims to provide guidelines and tips on cold-regions groundwater modeling for those with previous modeling experience.

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Section: Governing Equations and Characteristic Functionsmentioning

confidence: 99%

Guidelines for cold‐regions groundwater numerical modeling

et al. 2020

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“…In addition to the impact on the local environment, structures built on frozen soils are also subjected to frozen‐soil‐related problems, including frost heave (Tai et al, 2018), thaw settlement (Tai et al, 2020), frost jacking (Guo et al, 2016), and creep (Qi et al, 2013). Unfrozen water content fundamentally affects the hydromechanical behavior of frozen soil (Chen et al, 2006; Qi et al, 2013; Teng et al, 2020) and serves as an indicator of potential ice formation and moisture migration.…”

Section: Introductionmentioning

confidence: 99%

“…The calculation processes are complex and include many parameters, such as pore size distribution, which impedes the wide utilization of these models (Chai et al, 2018). Some empirical models fit the trend of experimental phase composition curve well and are easy to use (Anderson & Tice, 1972; Michalowski & Zhu, 2006); however, they generally fail to interpret the development of phase composition characteristics of saline soil (Teng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The phase composition curve of pore water is critical to analyze the thermo‐hydraulic processes of frozen soil in cold regions (Teng et al, 2020; Zhou et al, 2020). Based on the phase composition curve, several heat‐moisture coupled models have been developed for hydrologic research in cold regions (Harlan, 1973; Taylor & Luthin, 1978; Wu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Several theoretical and empirical phase composition curve models for pore waters of soils in cold regions have been proposed (Anderson & Tice, 1972; Dillon & Andersland, 1966; Michalowski & Zhu, 2006; Teng et al, 2020; Wang et al, 2017), some of which take the solute compositions into account (Banin & Anderson, 1974; Wan & Yang, 2020; Xu et al, 1995; Yong et al, 1979; Zhou et al, 2020). However, these models only include the water‐ice phase transition in the pore solution, and none of them consider the solution‐crystal phase transition.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Simplified Model for the Phase Composition Curve of Saline Soils Considering the Second Phase Transition

Wang

Liu

et al. 2021

Water Resources Research

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Phase composition curves are the basis for the thermo‐hydraulic analysis of saline soil in cold regions. Two types of phase transition, namely, the water‐ice and solution‐crystal phase transitions, would occur in saline soils due to the variation in temperature and migration of moisture and solute, and the occurrence order of the two types of phase transition is governed by the initial salt and water content. This study proposed a conceptual model for the water phase composition curve in saline soils by simplifying the correlation between pore solution properties and soil grain. This model was subsequently applied to the analysis of phase composition characteristics and explained the occurrence of a second phase transition in saline soil. The model was validated by the experimental data from known soils and showed both good results and ease of use when compared to three previous models. The conceptual model provides a convenient and intuitive way to understand the phase composition characteristics of pore solution in saline soil in cold regions and is also easy to use in the thermo‐hydraulic analysis such as the prediction of unfrozen water content and solute concentration.

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Use of an artificial neural network model for estimation of unfrozen water content in frozen soils

Ren

Fan

et al. 2022

Preprint

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A portion of pore water is typically in a state of unfrozen condition in frozen soils due to the complex soil-water interactions. The variation of the amount of unfrozen water and ice has a significant influence on the physical and mechanical behaviors of the frozen soils. Several empirical, semi-empirical, physical and theoretical models are available in the literature to estimate the unfrozen water content (UWC) in frozen soils. However, these models have limitations due to the complex interactions of various influencing factors that are not well understood or fully established. For this reason, in the present study, an artificial neural network (ANN) modeling framework is proposed and the PyTorch package is used for predicting the UWC in soils. For achieving this objective, extensive UWC data of various types of soils tested under various conditions were collected through an extensive search of the literature. The developed ANN model showed good performance for the test dataset. In addition, the model performance was compared with two traditional statistical models for UWC prediction on four additional types of soils and found to outperform these traditional models. Detailed discussions on the developed ANN model, and its strengths and limitations in comparison to different other models are provided. The study demonstrates that the proposed ANN model is simple yet reliable for estimating the UWC of various soils. In addition, the summarized UWC data and the proposed machine learning modeling framework are valuable for future studies related to frozen soils.

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Parameterization of soil freezing characteristic curve for unsaturated soils

Cited by 67 publications

References 37 publications

Guidelines for cold‐regions groundwater numerical modeling

Guidelines for cold‐regions groundwater numerical modeling

A Simplified Model for the Phase Composition Curve of Saline Soils Considering the Second Phase Transition

Use of an artificial neural network model for estimation of unfrozen water content in frozen soils

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