Bayesian analysis for learning and updating geotechnical parameters and models with measurements

Geotechnical planning and construction is typically associated with large uncertainties and limited data on site conditions. To describe the geotechnical performance as accurately as possible, it is thus necessary to combine information from different sources (site measurements, expert knowledge, and data from literature). The engineers collect a few hypotheses about site conditions and then gather field observations (e.g., measurements of deformations, stresses, or other relevant data) to identify the correct hypothesis. As we show in this chapter, this process can be formalized through Bayesian updating as part of a probabilistic reliability and risk assessment. Thereby, a prior probabilistic model is updated with the new data to a posterior probabilistic model, which is then the basis for further reliability and risk assessments. Bayesian updating has significant advantages over other methods for learning geotechnical models, due to its flexibility and the possibility to consistently combine data and observations from various sources with mechanical models and expert estimates.