Estimating Porosity and Particle Size for Hydraulic Conductivity of Binary Mixed Soils Containing Two Different-Sized Silica Particles

Choo, Hyunwook; Lee, W.; Lee, Changho; Burns, Susan E.

doi:10.1061/(asce)gt.1943-5606.0001802

Cited by 23 publications

(33 citation statements)

References 34 publications

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“…When R increases, the binary mixture packing starts to deviate from the ideal case, as the fine particles become large enough to disturb the original packing of the large particle and vice versa [15,33,44,45]. In this case, the porosity of the binary mixture becomes larger than the prediction of the ideal packing model (Figure 1a).…”

Section: Packing Model For Binary Mixturesmentioning

confidence: 96%

“…Prediction of porosity as function of fine particles mass fraction for: (a) coarse and fine sand mixture (data from Choo et al [33]); (b) sand-clay mixture (data from Marion et al [20]). The prediction of the ideal packing model is shown in dashed curve while the prediction of non-ideal packing model (Choo et al [33]) is shown in solid curve. All equations can be found in Choo et al [33].…”

Section: Packing Model For Binary Mixturesmentioning

confidence: 99%

“…In this case, the porosity of the binary mixture becomes larger than the prediction of the ideal packing model (Figure 1a). To correct for the underestimation of porosity in mixed soils, several semi-empirical models have been developed by introducing a mixing-degree coefficient (λ) into the ideal packing model [15,26,33]. The authors empirically related the mixing coefficient to R, based on data set from mixtures characterized by variable R, in an effort to provide an approach for predicting λ based on properties of end-members alone.…”

Section: Packing Model For Binary Mixturesmentioning

confidence: 99%

See 2 more Smart Citations

Improved Packing Model for Functionally Graded Sand-Fines Mixtures—Incorporation of Fines Cohesive Packing Behavior

El-Husseiny

2020

Applied Sciences

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Binary soil mixture, containing large silica particles (sand) mixed with variable content of very fine silt or clay, is an example of a functionally graded material that is important for several science and engineering applications. Predicting the porosity (or void ratio), which is a fundamental quantity that affects other physical properties, of such material as function of fines (clay or silt) fraction can be significant for sediment research and material design optimization. Existing analytical models for porosity prediction work well for binary mixed soils containing multi-sized non-cohesive particles with no clay, while such models frequently underestimate the porosity of sand-clay mixtures. This study aims to present an analytical model that accurately predicts the porosity of mixed granular materials or soils containing sand and very fine silt or clay (cohesive particles). It is demonstrated that accounting for the cohesive nature of very fine particles, which exists due to the effect of inter-particle forces, is a major missing aspect in existing packing models for mixed soils. Consequently, a previously developed linear packing model is modified so that it accounts for fines cohesive packing in sand-fines mixtures. The model prediction is validated using various experimental published data sets for the porosity of sand-fines mixtures. Improvement in the prediction of permeability and maximum packing dry density when incorporating cohesive packing behavior is discussed. The current model also provides important insights on the conditions under which, the lowest permeability and maximum packing state are expected.

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Section: Packing Model For Binary Mixturesmentioning

confidence: 96%

Section: Packing Model For Binary Mixturesmentioning

confidence: 99%

Section: Packing Model For Binary Mixturesmentioning

confidence: 99%

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Improved Packing Model for Functionally Graded Sand-Fines Mixtures—Incorporation of Fines Cohesive Packing Behavior

El-Husseiny

2020

Applied Sciences

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“…Some engineering applications, in which the grain shape and packing can be highly controlled, have found grain size analyses to be of value (Aubertin et al ; Mondal ; Henderson ; Côté et al ; Scannell ; Choo et al ). However, most of these studies, including many in the geotechnical literature, only report grain size K estimates for the purposes of relative comparisons, or comparisons between laboratory methods, and the absolute accuracy of the K values vis‐à‐vis field scale is not evaluated in detail (St. Marseille ; Rice ; Michette et al ).…”

Section: Introductionmentioning

confidence: 99%

Grain Size Analysis and Permeametry for Estimating Hydraulic Conductivity in Engineered Porous Media

Cormican

Devlin

Divine

2020

Groundwater Monitoring Rem

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Grain size analysis and permeametry are common methods for estimating the hydraulic conductivity (K) of porous media. It is well known that these methods have limited accuracy when they are used to characterize natural sediments. However, hydrogeological research has increasingly introduced technologies dependent on engineered porous media that may be less problematic because complex geologic structures are eliminated in the lab and field‐scale packings. The recently introduced Horizontal Reactive Media Treatment Wells (HRX® Wells), for in situ, passive remediation of groundwater is one such example. The HRX Well passively collects groundwater and directs it through a horizontal pipe packed with an engineered porous medium. In this project, grain size analysis was conducted for sand and sand‐iron mixtures to estimate K using the 16 algorithms provided in the HydrogeoSieveXL2.3.2 software. The results were compared to K determined by permeametry and a field‐scale column, 30 cm long and 25 cm in diameter, representing an HRX Well. The best comparability of K estimates from grain size analysis and permeametry were obtained using the USBR, Slichter, and Shepherd K estimation methods. These also showed good agreement between lab‐scale and field‐scale K estimations, with reproducibility within the range ±20%. This study shows that laboratory K estimations can be representative across various relevant scales, including the field‐scale, for engineered porous media. This finding extends to filter packs, and other engineered porous media design methods by emphasizing and demonstrating one case of accuracy in lab‐scale permeability estimation for field‐scale implementations.

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“…Physical clogging of soil filters is significant in the long-term performance of geotechnical and geoenvironmental infrastructure and is especially critical due to the complexity in predicting the reduction in pore space and conductivity caused by clogging. If the size of the pore can be predicted, then prediction of hydraulic conductivity, using existing equations/theories such as Kozeny Carman equation (Chapuis and Aubertin 2003;Choo et al 2018) and particle dynamics in cylindrical tubes (Reddi et al 2005;Happel and Brenner 2012; Won and Burns 2017) is relatively straight forward. In order to predict the clogging behavior and the reduction of hydraulic conductivity, the transport/retention behavior of particles in porous media should be studied.…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling of kaolinite transport through a sand filter

Won

Burns

2019

Can. Geotech. J.

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Accurately modeling the transport of clay particles through coarse-grained porous media is essential to engineering applications ranging from filtration and drainage, groundwater flow modeling, to contaminant transport. However, predicting the retention and clogging behavior of clay particles within a coarse-grained soil matrix is extremely challenging because clay particles can aggregate and form clusters with a variety of fabrics depending on the prevailing geochemistry of the pore fluid (i.e., pH and ionic strength). The work performed in this study developed a stochastic model to investigate the uncertainty of clay particle transport in porous media using random sampling at a given grain-size distribution to account for inherent uncertainty of the size of clay clusters being transported. Results demonstrated that the model proposed in this work can evaluate upper and lower boundaries of retention profiles of clay particles in a sand medium at given mean and standard deviation of grain-size distributions. In addition, the deterministic approach (using median sizes of sand and clay particles in the simulation) underestimated the mass of retained particles at small size ratios of clay particle size/sand particle size when compared with the stochastic prediction, which would result in nonconservative design.

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Estimating Porosity and Particle Size for Hydraulic Conductivity of Binary Mixed Soils Containing Two Different-Sized Silica Particles

Cited by 23 publications

References 34 publications

Improved Packing Model for Functionally Graded Sand-Fines Mixtures—Incorporation of Fines Cohesive Packing Behavior

Improved Packing Model for Functionally Graded Sand-Fines Mixtures—Incorporation of Fines Cohesive Packing Behavior

Grain Size Analysis and Permeametry for Estimating Hydraulic Conductivity in Engineered Porous Media

Stochastic modeling of kaolinite transport through a sand filter

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