Hygric characterization of wood fiber insulation under uncertainty with dynamic measurements and Markov Chain Monte-Carlo algorithm

摘要

The present work is the hygric characterization of wood fibre insulation boards, using dynamic measurements of relative humidity and sample weight, analyzed in the frame of Bayesian inference for parameter identification under uncertainty. It is an attempt at identifying detailed profiles of moisture dependent properties, and thus a relatively high number of parameters. Because of this ambition, some caution should be exercised once the outcome of the inversion algorithm is available: in addition to confidence intervals of parameters provided by the Bayesian framework, a simplified form of identifiability analysis is performed by analysing a posteriori parameter correlations and likelihood-based confidence intervals.