FRF-BASED PROBABILISTIC MODEL UPDATING IN STRUCTURAL DYNAMICS FOR UNCERTAINTY IDENTIFICATION AND QUANTIFICATION

In the recent years, probabilistic approaches have been developed to incorporate uncertainties in the dynamic systems. These uncertainties arise due to unknown experimental errors or variability in nominally identical dynamic systems. The majority of these probabilistic methods are based on modal data. These modal data based probabilistic methods do not employ damping matrices and hence cannot be used for accurate prediction of amplitudes of vibrations and complex frequency response functions (FRFs) and also these modal data based do not work well for the closed modes systems. In this paper, a new FRF-based parametric approach is presented which tackles the problem of incorporating damping and closed modes in uncertain dynamic systems. The advantages of using FRF data over modal data for probabilistic model updating are demonstrated. In the proposed FRF-based probabilistic updating approach, the finite element model is updated in such a way that the updated model reflects general damping in the experimental model by considering the updating parameters as complex. The effectiveness of the proposed finite element updating procedure is demonstrated by numerical examples. The results have shown that the proposed damped FRF-based probabilistic model updating procedure can be used to identify and quantify uncertainties in the dynamic systems.