An improved empirical equation for uniaxial soil compression for a wide range of applied stresses

An Improved Empirical Equation for Uniaxial Soil Compression for a Wide Range of Applied Stresses
D. D. Fritton* ABSTRACT
The response of soil to compaction forces is nonlinear and not completely described by existing statistical equations. The objective of this study was to find a better empirical equation for uniaxial soil compression. Disturbed and undisturbed samples from three to five horizons of four soils, and from soil mixed with four different amounts of sand, were subjected to applied stresses ranging from 0 to 2971 kPa at one to four initial water contents. Data from individual samples representing the three resulting curve shapes were used to evaluate existing and new empirical equations. A new equation was found that fit all three curve shapes better than any of the existing equations. The new equation fit data points of representative data sets with an average difference of 0.002 to 0.009 Mg m 3, compared with an average difference for two existing equations of 0.011 to 0.033 and 0.014 to 0.060 Mg m 3. The new equation was then fit to all 120 sets of experimental data, using nonlinear regression procedures. Regression relationships were established between three parameters that have traditionally been used to characterize soil compression (preconsolidation stress, compression index, and elastic rebound/recompression parameter) and the parameters of the new equation.

been represented by many equations. Koolen and Kuipers (1983) and Gupta and Allmaras (1987) discussed a number of these equations including the logarithmic equation used by Gupta and Larson (1982). The logarithmic equation in not able to fit data at applied stresses less than the preconsolidation stress—the point where the stress exceeds any previously experienced by the soil. Bailey et al. (1986) introduced an equation, ln( ) ln( o) (a b )(1
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t times, soil compaction significantly reduces crop yield. There is no routine procedure, though, to predict