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This study assesses the inherent variability in the geomaterial parameter by quantifying the parameter uncertainty and develops a site investigation plan with a low degree of uncertainty. A key research motivation was using sparse borehole data to predict a site geomaterial configuration in order to determine the design of a site investigation plan. This study develops a systematic methodology for carrying out a study of inherent variability in light of the limitations posed by borehole data. The data in this study was provided by the Iowa Department of Transportation which consisted of eight boreholes from which 92 associated SPT N-values was considered as the geomaterial parameter of interest. The systematic methodology then involved the following steps. A general linear model was employed to fit and compare various spatial covariance models with and without a nugget. These spatial covariance models were also evaluated with variograms. Predicted SPT N-values were generated using universal kriging. Simulations were performed conditionally and unconditionally to identify optimal site investigation plans. The results identified site investigation plans with reduced parameter uncertainty. The proposed approach can produce site investigation plans that target any or all geomaterial layers to reduce uncertainty with respect to any geomaterial parameter of interest.

Inherent variability assessment from sparse property data of overburden soils and intermediate geomaterials using random field approaches