Pore-scale model for estimating the bimodal soil–water characteristic curve and hydraulic conductivity of compacted soils with different initial densities

Chen, Renpeng; Liu, Peng; Liu, Xiaoming; Wang, Pengfei; Kang, Xin

doi:10.1016/j.enggeo.2019.105199

Cited by 37 publications

(20 citation statements)

References 54 publications

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“…Thereafter, k u slightly decreased to 2 × 10 −11 m/s at AEV S and then significantly decrease to 10 −14 m/s at RSV S . The low initial k u decrease is associated with discontinuous water flow and increased tortuosity between the lumps [69], whereas the final significant k u decrease is mainly attributed to water flow within the lumps only and may not be continuous across the lumps [70]. Beyond RSV S , flow occurs in vapor form due to evaporation of adsorbed water from clay particles within the lumps [11].…”

Section: Hydraulic Conductivitymentioning

confidence: 99%

“…Conversely, the fewer smaller pores released the remainder of capillary water and part of the adsorbed water only with the application of high suction values, that is, in the range of 700 kPa to 10 6 kPa [68]. Irrespective of the index property on the y-axis, the WRC comprised two air entry values and two residual suction values associated with capillary water drainage [69,70]: from larger pores (AEVL = 2 kPa and RSVL = 20 kPa) and from smaller pores (AEVS = 700 kPa and RSVS = 5 × 10 4 kPa). Beyond RSVS, vapor flow is dominant until complete dry conditions at 10 6 kPa [71], such that the adsorbed water is removed by evaporation [11].…”

Section: Water Retentionmentioning

confidence: 99%

“…Irrespective of the index property on the y-axis, the WRC comprised two air entry values and two residual suction values associated with capillary water drainage [69,70]: from larger pores (AEV L = 2 kPa and RSV L = 20 kPa) and from smaller pores (AEV S = 700 kPa and RSV S = 5 × 10 4 kPa). Beyond RSV S , vapor flow is dominant until complete dry conditions at 10 6 kPa [71], such that the adsorbed water is removed by evaporation [11].…”

Section: Volumetric Changesmentioning

confidence: 99%

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Effect of Composition on Engineering Behavior of Clay Tills

Paranthaman

Azam

2021

Geosciences

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Glacial geology, marine environment, and arid climate govern the composition of clay tills. The main purpose of this work is to develop a clear understanding of the engineering behavior of compacted clay till under soil suction and applied stress. The results indicate moderate water adsorption due to the presence of clay minerals (26% corrensite, 10% illite, and 8% kaolinite) with Ca2+ as the dominant cation and a flocculated fabric in a slightly basic (pH = 7.5) pore water. The water retention curve comprised four transition points that are associated with capillary water drainage from large pores (air entry value of 2 kPa and residual suction value of 20 kPa) and small pores (air entry value of 700 kPa and residual suction value of 5 × 104 kPa). Beyond the last value, vapor flow is dominant and removes the adsorbed water by evaporation. The ratio of soil volume change to water volume change best described the s-shaped shrinkage path that also comprised four stages, namely: from most large pores with low volume change; from remaining large and most small pores along with almost equal volume change; from some small pores with low volume change; and from the rest of the small pores with no volume change. Likewise, the s-shaped swelling potential curve comprised three stages and correlated well with bimodal hydraulic conductivity curve, that is, slow initial swelling (unsaturated hydraulic conductivity around 10−14 m/s) due to expansion of peripheral clay in lumps; high primary swelling (unsaturated hydraulic conductivity of up to 10−11 m/s) due to thickened double layer of most colloids; and slow secondary swelling (albeit unsaturated hydraulic conductivity around 10−10 m/s) due to expansion of remaining particles. Soil compression (compression index of 0.164) was due to a gradual reduction in number of large pores, whereas rebound (swelling index of 0.047) was due to water adsorption on clay with part of the deformations recovered. Finally, the consolidation rate was related to saturated hydraulic conductivity, which varied by three orders of magnitude.

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Section: Hydraulic Conductivitymentioning

confidence: 99%

Section: Water Retentionmentioning

confidence: 99%

Section: Volumetric Changesmentioning

confidence: 99%

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Effect of Composition on Engineering Behavior of Clay Tills

Paranthaman

Azam

2021

Geosciences

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“…Zhao et al [3] indicated that the bimodal compressibility behavior of well-graded sand with silt and gravel (SW-SM with gravel) was related to the bimodal SWCC. Chen et al [4] and Li et al [5] proposed bimodal permeability equations based on the bimodal SWCCs.…”

Section: Introductionmentioning

confidence: 99%

Bimodal SWCC and Bimodal PSD of Soils with Dual-Porosity Structure

Ding

Bian

Jun-ping

et al. 2022

Mathematical Problems in Engineering

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The soil–water characteristic curve (SWCC) and pore-size distribution (PSD) are fundamental characteristics of soils that determine many physical and mechanical properties. Recent studies demonstrate that both SWCC and PSD sometimes exhibit a bimodal feature. In this paper, soils with bimodal SWCCs are mainly divided into three categories: gap-graded soils, compacted clayey soils, and natural dual-porosity structural soils, from the perspective of microporosity structure. Based on the Fredlund and Xing unimodal SWCC equation, a bimodal SWCC equation is presented. The bimodal PSD equation d v / d log r for mercury intrusion porosimetry (MIP) is derived theoretically, according to the relationship between the mercury intrusion process in MIP and the water desorption process along the drying SWCC. These two associated equations have the same set of parameters, so the corresponding relationship between the bimodal SWCC and bimodal PSD can be directly shown. Three main presenting forms of PSD in MIP tests are summarized. Regression analysis results show that the proposed PSD equation can well fit bimodal PSD experimental data of various soils in the literature, and the SWCCs are predicted at the same time.

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“…Another conventional laboratory method for estimating SWRC is to use the particle size distribution (PSD) curve and volume-mass properties (Wen et al 2020). One of the theoretical methods is to apply the pore size distribution function (PoSD) and develop soil characteristic curve based on the soil pores network (Chen et al 2019;Fredlund et al 1994). It should be noted that unlike PSD, PoSD is affected by climatic conditions (temperature, soil moisture content, number of rainy or rainless months) and even could change with soil shrinkage (Wen et al 2021).…”

Section: Introductionmentioning

confidence: 99%