LAUSR

Abstract To accelerate the hygric characterisation of porous building materials, dynamic flow and storage measurements in combination with inverse parameter estimation show a lot of promise. Therein though, the processing and interpretation of the experimental output can be challenging. This paper demonstrates the applicability of two swarm-intelligence (SI) optimisers, i.e. the Particle Swarm Optimiser (PSO) and the Grey Wolf Optimiser (GWO), for determining the vapour resistance factor and the sorption isotherm of porous building materials. The methodology is presented for a fictitious dynamic vapour sorption experiment on a calcium silicate insulation sample. The identifiability of the unknown parameters and the reliability of the estimated properties is investigated via a profile likelihood (PL) analysis. By use of the proposed methodology, the measurement time required to determine the hygric properties is reduced strongly compared to standard techniques such as the steady-state cup and sorption tests, and at the same time the uncertainty propagated in the parameter estimation can be characterised. A close agreement with the target values is obtained. Though, to avoid an unreliable parameter estimation it is recommended to not limit the optimisation process to a single run. Furthermore, also the results obtained during the PL analysis can help improving the estimation. Finally, for the current case study, the SI optimisers are found to outperform the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES).