Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
716420 | IFAC Proceedings Volumes | 2012 | 6 Pages |
Abstract
This paper addresses an inverse approach to characterize the frequency-dependent elastic modulus of the polymer layer in laminated structures. Represented by fractional derivative models, the modulus is identified based on a finite element model of the laminated structure from the experimental frequency response functions. An efficient Markov Chain Monte Carlo method is implemented to learn the identification parameters from a Bayesian perspective. A surrogate model is applied to alleviate Bayesian computation through the use of artificial neutral network. The proposed approach is experimentally validated on a laminated glass.
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