Probabilistic Analysis of Soil-Water Characteristic Curves

Direct measurement of the soil-water characteristic curve (SWCC) is costly and time consuming. A first-order estimate from statistical generalization of experimental data belonging to soils with similar textural and structural properties is useful. A simple approach is to fit the data with a nonline...

Full description

Saved in:
Bibliographic Details
Published in:Journal of geotechnical and geoenvironmental engineering 2010-03, Vol.136 (3), p.445-455
Main Authors: Phoon, Kok-Kwang, Santoso, Anastasia, Quek, Ser-Tong
Format: Article
Language:English
Subjects:
Applied sciences
Buildings. Public works
Computation methods. Tables. Charts
Exact sciences and technology
Geotechnics
Structural analysis. Stresses
TECHNICAL PAPERS
Water effect, drainage, ground water lowering, filtration
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Direct measurement of the soil-water characteristic curve (SWCC) is costly and time consuming. A first-order estimate from statistical generalization of experimental data belonging to soils with similar textural and structural properties is useful. A simple approach is to fit the data with a nonlinear function and to construct an appropriate probability model of the curve-fitting parameters. This approach is illustrated using sandy clay loam, loam, loamy sand, clay, and silty clay data in Unsaturated Soil Database. This paper demonstrates that a lognormal random vector is suitable to model the curve-fitting parameters of the SWCC. Other probability models using normal, gamma, Johnson, and other distributions do not provide better fit than the proposed lognormal model. The engineering impact of adopting a probabilistic SWCC is briefly discussed by studying the uncertainty of unsaturated shear strength due to the uncertainty of SWCC.
ISSN:1090-0241
1943-5606
DOI:10.1061/(ASCE)GT.1943-5606.0000222