Establishing Soil-Water Characteristic Curve of a Fine-Grained Soil from Electrical Measurements

Soil suction (ψ) is one of the key factors that influence the resilient modulus (MR) of pavement subgrade soils. There are several models available in the literature for predicting the MR–ψ correlations. However, the various model parameters required in the existing models are generally determined by performing regression analysis on extensive experimental data of the MR–ψ relationships, which are cumbersome, expensive, and time-consuming to obtain. In this paper, a model is proposed to predict the variation of the MR with respect to the ψ for compacted fine-grained subgrade soils. The information of (i) the MR values at optimum moisture content condition (MROPT) and saturation condition (MRSAT), which are typically determined for use in pavement design practice; (ii) the ψ values at optimum moisture content condition (ψOPT); and (iii) the soil-water characteristic curve (SWCC) is required for using this model. The proposed model is validated by providing comparisons between the measured and predicted MR–ψ relationships for 11 different compacted fine-grained subgrade soils that were tested following various protocols (a total of 16 sets of data, including 210 testing results). The proposed model was found to be suitable for predicting the variation of the MR with respect to the ψ for all the subgrade soils using a single-valued model parameter ξ, which was found to be equal to 2.0. The proposed model is promising for use in practice, as it only requires conventional soil properties and alleviates the need for experimental determination of the MR–ψ relationships.

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A simplified method to estimate the soil-water characteristic curve

Canadian Geotechnical Journal ◽

10.1139/t10-033 ◽

2010 ◽

Vol 47 (12) ◽

pp. 1382-1400 ◽

Cited By ~ 51

Author(s):

Kheng-Boon Chin ◽

Eng-Choon Leong ◽

Harianto Rahardjo

Keyword(s):

Sensitivity Analysis ◽

Soil Water ◽

Characteristic Curve ◽

Estimation Methods ◽

Index Properties ◽

Soil Water Characteristic Curve ◽

Fine Grained ◽

Simplified Method ◽

Basic Index ◽

The One

This paper proposes a simplified method to estimate the soil-water characteristic curve (SWCC) for both coarse- and fine-grained soils using one-point SWCC measurement and basic index properties. Parameters of the Fredlund and Xing SWCC equation were correlated with the basic properties of 60 soils: 30 soils each of coarse- and fine-grained types. Sensitivity analysis revealed that the location of the one-point measurement at matric suctions of 10 and 500 kPa gave the most reliable SWCC using the proposed method for coarse- and fine-grained soils, respectively. The validity of the proposed method was evaluated using a total of 62 soils collated from published literature with 31 soils each of the coarse- and fine-grained types. The proposed method gives a good estimation of the SWCC and uses fewer parameters when compared with existing one-point SWCC estimation methods.

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Measurement of soil-water characteristic curves for fine-grained soils using a small-scale centrifuge

Canadian Geotechnical Journal ◽

10.1139/t02-060 ◽

2002 ◽

Vol 39 (5) ◽

pp. 1209-1217 ◽

Cited By ~ 41

Author(s):

R M Khanzode ◽

S K Vanapalli ◽

D G Fredlund

Keyword(s):

Water Content ◽

Soil Water ◽

Characteristic Curve ◽

Small Scale ◽

Characteristic Curves ◽

Pressure Plate ◽

Soil Water Characteristic Curve ◽

Fine Grained ◽

Soil Water Characteristic Curves ◽

Plate Apparatus

Considerably long periods of time are required to measure soil-water characteristic curves using conventional equipment such as pressure plate apparatus or a Tempe cell. A commercially available, small-scale medical centrifuge with a swinging type rotor assembly was used to measure the soil-water characteristic curves on statically compacted, fine-grained soil specimens. A specimen holder was specially designed to obtain multiple sets of water content versus suction data for measuring the soil-water characteristic curve at a single speed of rotation of the centrifuge. The soil-water characteristic curves were measured for three different types of fine-grained soils. The three soils used in the study were processed silt (liquid limit, wL = 24%; plasticity index, Ip = 0; and clay = 7%), Indian Head till (wL = 35.5%, Ip = 17%, and clay = 30%), and Regina clay (wL = 75.5%, Ip = 21%, and clay = 70%). The soil-water characteristic curves for the above soils were measured in 0.5, 1, and 2 days, respectively, using the centrifuge technique for suction ranges from 0 to 600 kPa. Time periods of 2, 46, and 16 weeks were required for measuring the soil-water characteristic curves for the same soils using a conventional pressure plate apparatus. There is reasonably good agreement between the experimental results obtained by the centrifuge and the pressure plate methods. The results of this study are encouraging as soil-water characteristic curves can be measured in a reduced time period when using a small-scale centrifuge.Key words: unsaturated soils, soil-water characteristic curve, centrifuge technique, soil suction, matric suction, water content.

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Prediction of the soil-water characteristic curve based on the specific surface area of fine-grained soils

Bulletin of Engineering Geology and the Environment ◽

10.1007/s10064-014-0664-y ◽

2014 ◽

Vol 74 (3) ◽

pp. 697-703 ◽

Cited By ~ 9

Author(s):

Bojana Dolinar

Keyword(s):

Specific Surface ◽

Soil Water ◽

Surface Area ◽

Specific Surface Area ◽

Characteristic Curve ◽

Soil Water Characteristic Curve ◽

Fine Grained

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Measurement and estimation of pore shrinkage and pore distribution in a clayey till during soil-water characteristic curve tests

Canadian Geotechnical Journal ◽

10.1139/t01-014 ◽

2001 ◽

Vol 38 (4) ◽

pp. 741-754 ◽

Cited By ~ 90

Author(s):

Paul H Simms ◽

Ernest K Yanful

Keyword(s):

Pore Size Distribution ◽

Pore Size ◽

Size Distribution ◽

Soil Water ◽

Mine Waste ◽

Characteristic Curve ◽

Landfill Liners ◽

Soil Water Characteristic Curve ◽

Fine Grained ◽

In Series

The soil-water characteristic curve (SWCC) of fine-grained soils is usually determined experimentally. In many applications, such as design of mine waste covers and landfill liners, the unsaturated permeability function, k(h), is often derived theoretically from the measured SWCC. Implicit in these derivations is the transformation of the SWCC to a pore-size distribution (PSD), typically assumed to be constant and mono-modal. PSDs of a clayey till compacted at various water contents were measured after compaction, after flexible-wall permeability testing, and during and after SWCC tests. The measurements show that the PSD changes significantly during permeability and SWCC testing. A method is advanced for predicting the observed changes in PSD during SWCC testing. PSDs are determined for soil samples subjected to the highest and lowest suctions applied during the SWCC test. The measured PSDs are transformed to account for pore trapping; the transform assumes that flow occurs through two sets of randomly distributed pores in series. To model pore shrinkage, the pores are idealized as elastic cylinders. PSDs measured after different suction applications in the SWCC tests are compared with predictions of the shrinkage model. The method can also be used to predict the SWCC. Measured and predicted values are compared.Key words: landfill liners, mine waste covers, soil-water characteristic curve, pore-size distribution.

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Integrated approaches for predicting soil-water characteristic curve and resilient modulus of compacted fine-grained subgrade soils

Canadian Geotechnical Journal ◽

10.1139/cgj-2016-0349 ◽

2017 ◽

Vol 54 (5) ◽

pp. 646-663 ◽

Cited By ~ 14

Author(s):

Zhong Han ◽

Sai K. Vanapalli ◽

Wei-lie Zou

Keyword(s):

Moisture Content ◽

Soil Water ◽

Resilient Modulus ◽

Characteristic Curve ◽

Experimental Studies ◽

Pavement Design ◽

Soil Water Characteristic Curve ◽

Subgrade Soils ◽

Fine Grained ◽

Integrated Approaches

This paper combines a series of approaches for predicting the soil-water characteristic curve (SWCC) and the variation of the resilient modulus (MR) of compacted fine-grained subgrade soils with moisture content, which is the key information required in mechanistic pavement design methods. The presented approaches for the SWCC and MR are integrated, as (i) they are developed following the same philosophy, (ii) they require only the measurements of the suction and moisture content or MR at saturated and optimum moisture content conditions for prediction, and (iii) the predicted SWCC is used for predicting the MR – moisture content relationship. Experimental studies have been performed on five fine-grained subgrade soils that were collected from different regions in Ontario, Canada, to determine their MR at various external stress levels and post-compaction moisture contents, as well as their SWCCs after the MR tests. Experimental measurements are predicted using the integrated approaches and the empirical approaches currently used in the mechanistic–empirical pavement design guide (MEPDG). It is demonstrated that the integrated approaches are easy to use and show improved reliability in predicting both the SWCC and MR for the investigated subgrade soils in spite of using limited experimental data.

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Estimating soil-water characteristic curve based on soil type and best-fitting regressions derived from a simplified method using Aburra Valley dataset

MATEC Web of Conferences ◽

10.1051/matecconf/202133702002 ◽

2021 ◽

Vol 337 ◽

pp. 02002

Author(s):

Johnatan Ramos-Rivera ◽

Daniel Parra-Holguín ◽

Yamile Valencia-González ◽

Oscar Echeverri-Ramírez

Keyword(s):

Machine Learning ◽

Soil Water ◽

Soil Mechanics ◽

Characteristic Curve ◽

Computer Code ◽

Data Set ◽

Soil Water Characteristic Curve ◽

Fine Grained ◽

Simplified Method ◽

Unsaturated Soil Mechanics

In unsaturated soil mechanics, many attempts have been made to estimate the SWCC based on soil texture and grain-size distribution. This paper proposes a simplified method to estimate the soil-water characteristic curve (SWCC) for both coarse and fine-grained soils using SWCC data and machine learning computer code in the Aburra Valley. Fredlund and Xing parameters has been used to estimate the SWCC correlations. Soil samples collected from field survey were subjected to laboratory testing, SWCCs were estimated using filter paper method. Each SWCC data set from Aburra Valley was fitted with Fredlund and Xing curve using multiple regression analysis, correlations were derived for those four parameters based on predictors derived from machine learning. The proposed method gives a good estimation and low residual errors of the SWCC.

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