Robust probabilistic modelling of mould growth in building envelopes using random forests machine learning algorithm
(2023) In Building and Environment 243.- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/55fa3349-8b6a-41c3-9d86-7c8b66b83747
- author
- Bayat Pour, Mohsen LU ; Niklewski, Jonas LU ; Naghibi, Seyed Amir LU and Frühwald Hansson, Eva LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Building envelope, Hygrothermal simulation, Machine learning, Mould assessment, Reliability analysis, Sensitivity analysis
- in
- Building and Environment
- volume
- 243
- article number
- 110703
- pages
- 15 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85167434242
- ISSN
- 0360-1323
- DOI
- 10.1016/j.buildenv.2023.110703
- language
- English
- LU publication?
- yes
- id
- 55fa3349-8b6a-41c3-9d86-7c8b66b83747
- date added to LUP
- 2023-08-12 23:13:39
- date last changed
- 2023-10-16 15:36:06
@article{55fa3349-8b6a-41c3-9d86-7c8b66b83747,
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.}},
author = {{Bayat Pour, Mohsen and Niklewski, Jonas and Naghibi, Seyed Amir and Frühwald Hansson, Eva}},
issn = {{0360-1323}},
keywords = {{Building envelope; Hygrothermal simulation; Machine learning; Mould assessment; Reliability analysis; Sensitivity analysis}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Building and Environment}},
title = {{Robust probabilistic modelling of mould growth in building envelopes using random forests machine learning algorithm}},
url = {{http://dx.doi.org/10.1016/j.buildenv.2023.110703}},
doi = {{10.1016/j.buildenv.2023.110703}},
volume = {{243}},
year = {{2023}},
}