Probabilistic assignment of design strength for sands from in-situ testing data.

This paper proposes a simple design code format for assigning design values of effective friction angle of clean sands from in-situ tests incorporating all sources of geotechnical uncertainty, including model uncertainty. New transformation models are proposed using a high-quality database of undisturbed sands. The quantitative characterization of uncertainty is pursued using a Bayesian hybrid Markov Chain Monte Carlo simulation framework. Design factors are calibrated using the posterior distributions output by the Bayesian framework. The effects of the aleatory and epistemic components of geotechnical uncertainty are discussed and assessed quantitatively. The paper concludes by demonstrating how to apply the results practically in geotechnical design.