Robust probabilistic modelling of mould growth in building envelopes using random forests machine learning algorithm: Fid-Bau Portal

Robust probabilistic modelling of mould growth in building envelopes using random forests machine learning algorithm

Bayat Pour, Mohsen / Niklewski, Jonas / Naghibi, Amir / Frühwald Hansson, Eva

Abstract Probabilistic methods can be used to account for uncertainties in hygrothermal analysis of building envelopes. This paper presents methods for robust mould reliability analysis and identification of critical parameters. Mould indices are calculated by probabilistic hygrothermal analysis, followed by the application of the "Finnish mould growth model." To increase the robustness of the mould growth analysis, a random forests metamodel is first trained on the dataset and then used to expand the number of simulations. Finally, the reliability is calculated based on the probability of exceeding a given maximum mould index limit state. Critical parameters are identified through a sensitivity analysis based on linear and non-linear dependencies between inputs and maximum mould index. The methods are demonstrated by analysing three external wall assemblies. In conclusion, the mould reliability analysis method helps to assess the robustness of the hygrothermal analysis and mould assessment by investigating the influence of hygrothermal variables' uncertainties on the maximum mould index. By combining a metamodel with probabilistic analysis, it is possible to significantly reduce the amount of time required to evaluate a large number of scenarios.

Highlights • Mould reliability analysis method assesses the robustness of mould analysis. • The metamodel helps to achieve a more robust mould analysis considerably faster. • Mould sensitivity analysis method investigates linear and nonlinear sensitivities. • Segmented sensitivity analysis assesses the condition for mould growth germination.