Title: Bayesian approaches for parameter identification and damage detection

Organiser: Francesca Marsili (Helmut Schmidt Universität), Filippo Landi (University of Pisa) 

Bayesian updating techniques are an appropriate approach to solve inverse problems, that is, to improve knowledge of random parameters that are not directly observable through measurements of parameters related to them with a mathematical model. Such techniques have several advantages, for example, they are model agnostic and do not depend on the type of quantities measured. In addition, their application allows not only for the detection of changes in the unobservable parameters but also for the quantification of their magnitude. This session provides an overview of recent advances in Bayesian updating algorithms and their application to SHM for parameter identification and damage detection.