Hierarchical Bayesian modelling for geotechnical parameter derivation
A. Mavritsakis (Deltares)
Timo Schweckendiek (TU Delft - Hydraulic Structures and Flood Risk, Deltares)
A.M. Teixeira (Deltares)
E. Smyrniou (Deltares)
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Abstract
Bayesian inference poses as a means for characterizing the uncertainty in geotechnical parameters based on limited site investigation data. In this study, a Hierarchical Bayesian analysis framework is used to analyse a site investigation database in order to derive geotechnical soil parameters for two widely applied strength models. The first one focuses on calibrating the relationship between in-situ CPT measurements and undrained shear strength. The second one is the SHANSEP soil strength model, which is used forevaluating the undrained shear strength using OCR information. The framework operates in a hierarchical fashion, performing inference on separate project sites and at the same time drawing conclusions on a global level. The result is site characterization on a probabilistic level and the derivation of geotechnical parameters together with their probability distributions. The results are assessed by evaluating their influence in the failure probability of a geotechnical structure, demonstrating that the proposed hierarchical approach provides a more complete description of uncertainty than standard practice methods.