Bayesian characterization of Young's modulus of viscoelastic materials in laminated structures

Abstract This paper addresses an inverse approach to estimate the frequency-dependent Young's modulus of a viscoelastic polymer layer in a laminated structure. The Young's 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.