The influence of experimental and sampling uncertainties on the probability of unsatisfactory performance in geotechnical applications

Abstract

While the effect of spatial variability on the probability of unsatisfactory performance in geotechnical applications is relatively well understood, comparatively less attention has been given in the literature to the effects of experimental (measurement scatter) and sampling (insufficient number of samples) uncertainties. In this paper, a general approach is developed to incorporate experimental and sampling uncertainties into probabilistic analyses based on random field methods. It is shown that, when compared with the standard approach which attributes the measured total soil variability to spatial variability, consideration of experimental uncertainty may significantly reduce the calculated probability of unsatisfactory performance. It is argued that this may be one of the reasons for an overestimation of the probability of unsatisfactory performance in geotechnical probabilistic simulations (another important reason is the spatial averaging of soil properties). Evaluation of the sampling uncertainty reveals that, although a relatively large number of samples is needed for spatial variability characterisation, a limited number of samples is sufficient to quantify the experimental uncertainty. It is pointed out that no adjustments of the existing random field-based software are needed to consider the additional uncertainties. To illustrate the proposed approach, two extensive experimental data sets on sand are presented: one reflecting total variability and the other quantifying experimental uncertainty. A hypoplastic model is calibrated against the two data sets and adopted in random field analyses of strip footing settlement.