MS24 Bayesian Analysis of Structural and Geotechnical Models
Dr. Iason Papaioannou: email@example.com
Iason Papaioannou, Dr., Technical University of Munich, Germany, firstname.lastname@example.org
Costas Papadimitriou, Prof., University of Thessaly, Greece, email@example.com
Daniel Straub, Prof., Technical University of Munich, Germany, firstname.lastname@example.org
Jie Zhang, Prof., Tongji University, China, email@example.com
Abstract of the special session:
Design and assessment of engineering systems is often based on numerical models of physical systems and involved processes. The parameters of the numerical models are determined by combining information from different sources such as direct measurements of the parameters or the system behavior, expert knowledge, categorical data and information from literature. In probability theory, the process of combining information to learn model parameters is formalized in the concept of Bayesian updating.
Thereby, the prior probability distribution of the model parameters is updated with new data to a posterior distribution. The derived distribution can be further used for forward uncertainty propagation and reliability assessment of the system performance. This mini-symposium aims to attract papers that address either methodological developments or novel applications on Bayesian analysis of numerical models of structural and geotechnical systems. Individual relevant topics include: Markov chain Monte Carlo methods; sequential Montel Carlo methods; Taylor series approximations to the posterior; conjugate priors and Gibbs sampling; approximate Bayesian computation; structural identification; reliability updating; updating in the presence of spatial/time variability; updating of meta-models; applications that investigate the influence of prior considerations on the analysis results; definition of the likelihood function; representation of model errors; optimal experimental design.
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