Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges

• Novel methodology for the identification of stochastic models of uncertainties.
• Linear computational structural dynamics for the low-and medium-frequency ranges.
• Uncertain system parameters and model uncertainties induced by modeling errors.
• Bayesian posteriors of uncertainty quantification in computational structural dynamics.

The paper is devoted to the modeling and identification of uncertainties in computational structural dynamics for low-and medium-frequency ranges. A complete methodology is presented for the identification procedure. The first eigenfrequencies are used to quantify the uncertainties in the low-frequency band while the frequency response functions are used to quantify the uncertainties in the medium-frequency band. The system-parameter uncertainties are taken into account with the parametric probabilistic approach. The model uncertainties are taken into account with the nonparametric probabilistic approach. The posterior stochastic model of system-parameter uncertainties is identified using the Bayes method.