Many materials in modern civil engineering applications, such as coated carbon reinforcement grids or PVB-interlayers for safety glass, are polymer-based. These materials are showing distinct viscoelastic and temperature dependent behaviour. In literature, different mathematical representations of these phenomena exist. A common one is the Prony-series representation, which is implemented in many state-of-the-art Finite-Element-Analysis-Software. The Prony-parameters can either be determined by relaxation or retardation experiments in the time domain or with a steady state oscillation in the frequency domain.
However, present research shows that polymeric materials also may need to have constitutive equations which include hyperelasticity when undergoing large deformations and which also may respect the Mullins- or Payne-Effect, so that the material model should be expanded for a more realistic representation in numerical simulations.
A novel method for the whole identification process of a numerical material model based on experimental data within a stochastic framework will be presented. It will be shown that the Bayesian inference approaches are advantageous over deterministic approaches as they allow incomplete and noisy measurement data and quantitatively assess uncertainties and sensitivity. The findings are demonstrated via examples from engineering practice.