The use of deterministic models to predict the behavior of systems is currently a common practice adopted in engineering. However, these predictions always present certain level of uncertainties related mainly to: (1) uncertain nature of excitations, (2) simplifications in mathematical models and (3) lack of information about the values of the parameters used in the mathematical models. These uncertainties must be taken into account if more robust predictions are required, especially to identify optimal designs or to improve the prediction of systems that are already in operation.
The characterization of uncertainties by means of a stochastic framework allows performing analyzes aimed at quantifying the probabilities of having a specific performance (estimates of failure probability and risk assessment), optimizing performance variables (risk minimization) or aimed at improving the prediction of systems with data monitoring (implementations in the monitoring of structures). Motivated by the importance of conducting this type of analysis, the objective is to contribute in the design area through the development of efficient computational methodologies for the quantification of uncertainties, based mainly on the use of stochastic simulation, Bayesian analysis and implementation of metamodels.
This group is formed in the Department of Civil Engineering of the Faculty of Physical and Mathematical Sciences of the University of Chile with the idea of serving as a platform to generate interdisciplinary collaborations, at the same time to disseminate the progress in the field.