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.