Publication de 6 numéros par an
ISSN Imprimer: 2152-5080
ISSN En ligne: 2152-5099
Indexed in
STOCHASTIC SAMPLING BASED BAYESIAN MODEL UPDATING WITH INCOMPLETE MODAL DATA
RÉSUMÉ
In this paper, we are interested in model updating of a linear dynamic system based on incomplete modal data including modal frequencies, damping ratios, and partial mode shapes of some of the dominant modes. To quantify the uncertainties and plausibility of the model parameters, a Bayesian approach is developed. The mass and stiffness matrices in the identification model are represented as a linear sum of the contribution of the corresponding mass and stiffness matrices from the individual prescribed substructures. The damping matrix is represented as a sum of the contribution from individual substructures in the case of viscous damping, in terms of mass and stiffness matrices in the case of classical damping (Caughey damping), or a combination of the viscous and classical damping. A Metropolis-within-Gibbs sampling based algorithm is proposed that allows for an efficient sampling from the posterior probability distribution. The effectiveness and efficiency of the proposed method are illustrated by numerical examples with complex modes.
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Bansal Sahil, Cheung Sai Hung, Stochastic Simulation Algorithm for Robust Reliability Updating of Structural Dynamic Systems Based on Incomplete Modal Data, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3, 4, 2017. Crossref
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Bansal Sahil, Cheung Sai Hung, On the Bayesian sensor placement for two-stage structural model updating and its validation, Mechanical Systems and Signal Processing, 169, 2022. Crossref