Bayesian Uncertainty Quantification and Propagation (UQ+P): State-of-the-Art Tools for Linear and Nonlinear Structural Dynamics Models

Abstract

A Bayesian framework for uncertainty quantification and propagation in complex structural dynamics simulations using vibration measurements is presented. The framework covers uncertainty quantification techniques for parameter estimation and model selection, as well as uncertainty propagation techniques for robust prediction of output quantities of interest in reliability and safety of the structural systems analyzed. Bayesian computational tools such as asymptotic approximation and sampling algorithms are presented. The Bayesian framework and the computational tools are implemented for linear and nonlinear finite element models in structural dynamics using either identified modal frequencies, measured response time histories, or frequency response spectra. High performance computing techniques that drastically reduce the excessive computational demands that arise from the large number of system simulations are outlined. Identified modal properties from a full-scale bridge demonstrate the use of the proposed framework for parameter estimation of linear FE models.