Abstract

The quantification of the spatial variability of soil properties allows the enhanced engineering modelling, analysis, and design of geotechnical systems. Evolutionary design codes such as Eurocode 7 are awarding spatial variability an increasing central role in geotechnical design. The spatial variability of geotechnical properties is often investigated using a random field approach. Among the defining parameters of a random field is the scale of fluctuation, which describes the extent of significant spatial correlation in a specific spatial direction. The scale of fluctuation can be estimated quantitatively using a variety of methods relying on statistical approaches. The scale of fluctuation is not an inherent property of a soil. Existing studies demonstrate its dependency from numerous factors including the spatial direction, measurement interval, and user-defined modelling options. This paper illustrates the procedures and main results of the comparative estimation of the vertical scale of fluctuation of undrained shear strength of a layer of silty clay from piezocone (CPTU) and dilatometer (DMT) testing at a rural site in the region of Tuscany in central Italy. Vertical scales of fluctuation were calculated using two methods available in the geotechnical literature. Quantitative estimates are compared and analysed critically.

Abstract

Using CPTu profiles for subsoil characterisation, transformation equations must be used to obtain the hydro-mechanical properties for structures and infrastructure designing. Additionally, the uncertainty and the spatial variability of measured parameters must be taken into account for a reliable geotechnical design. In this work, we used a Stochastic Simulation approach to define reliable 3D models of two geotechnical designing variables for granular soils (friction angle–’ and the Darcy permeability coefficient–k) from tip resistance (qc), sleeve friction (fs), and pore pressure (u2) profiles. The selected method – the Sequential Gaussian Co-Simulation (SGCS) – provided reliable optimized 3D models of the spatial distribution of the variables of interest and allowed quantifying the propagation of the estimation uncertainty associated with the raw measurement models through the transformation equations. Overestimation (OE) and Underestimation (UE) percentages for a confidence interval of 68% were calculated throughout the 3D model: granular soils showed a larger uncertainty than fine soils concerning the measured variables (qc, fs, and u2). In granular soils, the measured variable uncertainty varies up to 100% but the derived variables show different behavior: ’ shows UE and OE less than 25% while k reaches 100%. These differences in the propagated uncertainties depend on the transformation equations and the measured variable dependence.