Publication | Closed Access
Prediction of the Soil-Water Characteristic Curve Based on Grain-Size-Distribution and Index Properties
93
Citations
8
References
2005
Year
Unknown Venue
Pore Size DistributionHighway PavementPavement EngineeringPrecision AgricultureEngineeringPlasticity IndexSoil-water Characteristic CurveEarth ScienceSoil MechanicGeotechnical EngineeringSoil PropertySoil DynamicsGeoenvironmental EngineeringSoil EngineeringSoil PropertiesHydrogeologyMoisture ContentIndex PropertiesSoil Physical QualitySoil PhysicCivil Engineering MaterialsHydrologyUnsaturated Soil MechanicsSoil ModelingEnvironmental EngineeringCivil EngineeringGeomechanicsConstruction Engineering
The grain-size-distribution (GSD) of a soil is intimately related to its pore size distribution and hence, the GSD holds a close relation with the soil-water characteristic curve (SWCC). In addition, the plasticity index (PI) is a measure of the water holding capacity of the soil and therefore, it plays an important role in shaping the SWCC. This paper presents two sets of statistically derived equations that describe the SWCC of non-plastic and plastic soils. Data from 154 non-plastic soils and 63 plastic soils were analyzed. Soil samples were collected as part of the National Cooperative Highway Research Program (NCHRP) 9–23 project entitled Environmental Effects in Pavement Mix and Structural Design Systems. Samples were obtained from underneath paved roads of 30 sites located throughout the United States. The soil samples were subjected to laboratory testing that included index testing and SWCC testing. SWCCs were determined using a newly developed pressure plate device capable of overburden pressure application, continuous measurements of moisture content, and volume change monitoring. In addition to the collected field data, a database of published soil index properties and SWCCs was incorporated to the analysis. Each SWCC data set was fitted with Fredlund and Xing curve, which provided an S-shaped curve with four parameters, af, bf, cf, and hrf. Using multiple regression analysis, equations were derived for these four parameters based on predictors derived from GSD and PI. The equations presented in this paper are useful in predicting the SWCC of any given soil without carrying out actual SWCC testing and they can easily be incorporated into computer codes to solve various unsaturated soil mechanics problems such as determining moisture beneath covered areas.
| Year | Citations | |
|---|---|---|
Page 1
Page 1