Bayesian characterization of Youngʹs modulus of viscoelastic materials in laminated structures

This paper addresses an inverse approach to estimate the frequency-dependent Youngʹs modulus of a viscoelastic polymer layer in a laminated structure. The Youngʹs modulus is parameterized by a fractional derivative model and examined from a Bayesian perspective with the consideration of measurement and modeling uncertainties. The probabilistic Bayesian identification is carried out based on an efficient surrogate model through the use of Markov Chain Monte Carlo sampling methods. The proposed approach is experimentally validated on laminated glass.