  A common approach to assess the performance of fire insulation panels is the
component additive method (CAM). The parameters of the CAM are based on the
temperature-dependent thermal material properties of the panels. These material
properties can be derived by calibrating finite element heat transfer models
using experimentally measured temperature records. In the past, the calibration
of the material properties was done manually by trial and error approaches,
which was inefficient and prone to error. In this contribution, the calibration
problem is reformulated in a probabilistic setting and solved using the
Bayesian model calibration framework. This not only gives a set of best-fit
parameters but also confidence bounds on the latter. To make this framework
feasible, the procedure is accelerated through the use of advanced surrogate
modelling techniques: polynomial chaos expansions combined with principal
component analysis. This surrogate modelling technique additionally allows one
to conduct a variance-based sensitivity analysis at no additional cost by
giving access to the Sobol' indices. The calibration is finally validated by
using the calibrated material properties to predict the temperature development
in different experimental setups.
