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Fitting Uniaxial Soil Compression Using Initial Bulk Density, Water Content, and Matric Potential
Author(s) -
Fritton D. D.
Publication year - 2006
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2005.0247
Subject(s) - water content , bulk density , soil water , soil science , mathematics , nonlinear regression , nonlinear system , compaction , compression (physics) , pedotransfer function , water potential , clay soil , soil texture , geotechnical engineering , materials science , environmental science , regression analysis , geology , physics , statistics , composite material , hydraulic conductivity , quantum mechanics
The existing bulk density and wetness of a soil influence soil compaction, but the ability to quantitatively describe these effects is limited. The purpose of this study was to generalize an empirical nonlinear equation for uniaxial soil compression, presently limited to fitting one compression curve at a time, by adding a capability to describe a single soil material at variable initial bulk density and wetness. The original relationship between the compressed soil bulk density and the applied stress was first simplified by reducing the number of coefficients required to fit a single experimental curve from three to two. The generalized relationship combined the original equation, an equation that removed a high correlation that existed between two of its coefficients, and two equations that reflected the impact of the bulk density, water content, and matric potential on the two remaining coefficients. This generalized relationship was tested for soil materials at various initial bulk density, water content, and matric potential values where texture and organic matter content were held constant. The study used 21 datasets representing three to five horizons of four soils and one soil mixed with four different amounts of sand. For each horizon or soil material studied, the generalized relationship was fitted simultaneously using nonlinear regression to 2–14 compression curves per horizon, including disturbed and undisturbed soil samples in nine cases. The generalized relationship fit each dataset with an R 2 ≥ 0.932 ( P < 0.001) and was judged superior to the best existing equation for multiple curves.