Bayesian methodology for updating geomechanical parameters and uncertainty quantification

Abstract

A generic framework for the updating geomechanical parameters is presented. It is based on Bayesian probabilities, and considers several levels of uncertainty. It allows one to properly update the probability distribution function of a given parameter when new data are available. This framework is applied to the case of deformability modulus updating in a large underground structure. Two different approaches were tested in terms of initial knowledge about the parameter, namely no knowledge, and a prior distribution of the parameter based on geological/geotechnical data and application of analytical solutions based on the empirical classification systems. The updating was carried out using the framework together with the results of high quality in situ tests. The Bayesian framework was shown to be a mathematically valid way to deal with the problem of the geomechanical parameter updating and of uncertainty reduction related to the parameter’s real value.