Hierarchical Bayesian uncertainty quantification of Finite Element models using modal statistical information

This paper develops a Hierarchical Bayesian Modeling (HBM) framework for uncertainty quantification of Finite Element (FE) models based on modal information. uses an existing Fast Fourier Transform (FFT) approach to identify experimental parameters from time-history data and employs class maximum-entropy probability distributions account the mismatch between parameters. It also considers parameterized distribution capturing variability structural across multiple sets. In this framework, computation is addressed through Expectation-Maximization (EM) strategies, empowered by Laplace approximations. As result, new rationale introduced assigning optimal weights properties when updating According features’ are equal inverse aggregate uncertainty, comprised identification prediction uncertainties. The proposed coherent in modeling entire process inferring response-only measurements comprehensive accounting different sources including both over sets, as well their Numerical examples employed demonstrate HBM wherein environmental operational conditions almost constant. observed that sets remains dominant source while being much larger than