Nonlinear Determination of Suction Compression Index in Expansive Soils for Heave Prediction

Climatic conditions easily allow for volume change of expansive soils. This happens due to the wetting and drying cycles that affect the moisture active zone. The suction compression index (${\gamma }_h$) is the key parameter that relates volumetric changes to soil suction changes in unsaturated soils. It is defined as the slope of the suction–volumetric strain relationship. This relationship is essentially nonlinear and the slope is determined for an idealized form of the relationship. Therefore, the need for a precise ${\gamma }_h$ determination method has always been in demand. The more accurate ${\gamma }_h$ is determined, the more accurate soil movements can be predicted and taken care of early in the design stage of a project. Accordingly, more money can be saved from either the repair costs or the initial costs by avoiding overdesign. The purpose of this paper is to introduce an original testing method for ${\gamma }_h$ determination. The testing method uniquely incorporates volumetric and suction measurements in a new and practical way utilizing simple digital imaging. The testing method unprecedentedly integrates statistical modeling for determination of incremental ${\gamma }_h$ in order to cover the entire nonlinearity of the suction–volumetric strain relationship. This is done by fitting the S-shaped relationship by a well-known class of statistical functions called cumulative distribution functions (CDFs). Incremental ${\gamma }_h$ is estimated by estimating the CDF at every suction value. The appropriateness of using these estimates to describe the suction–volumetric strain relationship is evaluated using the Kolmogorov–Smirnov (K–S) goodness of fit test. Furthermore, 95% confidence intervals of the superposed curves are also used to assess the appropriateness of the CDF estimates. The new testing method is compared against other techniques in the literature and proven reliable results.