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Multiple model updating classes are defined based on different subsets of modes and different weight factors.
Structural damage is then identified using Bayesian model class selection and model averaging techniques over the results of all the considered model updating classes.
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Based on the field test data and the modified modeling method, this study puts forward the time-domain Markov chain Monte Carlo (MCMC -based Bayesian MCMC -baseding and model class selection method for identification of the rail-sleeper-Bayesiansystemodel
Finite element (FE) model updating technique belongs to the class of inverse problems in classical mechanics.
Within this context, FE model updating technique, which belongs to the class of inverse problems in classical mechanics, is used to detect, locate and quantify damage.
In two illustrative applications, it is demonstrated that Bayesian model class selection can be effectively applied to this end, ensuring more realistic modeling and corresponding Bayesian model updating results.
It is found that the identifiability of the model updating problem depends very much on the complexity of the class of models.
In the second phase, Bayesian model updating is adopted to calculate the posterior PDF of uncertain model parameters using the selected model class from the first phase.
Model updating is, therefore necessary so that updated model should replicate the physical system.
In the first step, mass and stiffness matrices are updated using FRF-based model updating method.
Compared with the traditional method, the multi-state model updating method got a similar updating result.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com