Parametric identification of elastic modulus of polymeric material in laminated glasses

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.