Modelling wood moisture content in outdoor conditions from measured data
(2024) 55th Annual meeting of the IRGWP In Proceedings IRG Annual Meeting 2024.- Abstract
- Sustainable use of wood requires an understanding of expected service life, particularly when the material is exposed to outdoor conditions and, thus, fungal decay. Since moisture is the primary vector for fungal decay, accurate moisture prediction is a key component in service life assessment. For this purpose, the present study leverages existing measured data for linear regression of in-field moisture conditions of different wood species against climate parameters. Predictors of precipitation, relative humidity, and temperature were used in a finite distributed lag model to account for present and previous weather records. Issues of collinearity were addressed by ridge regression. The resulting model was, in general, able to describe... (More)
- Sustainable use of wood requires an understanding of expected service life, particularly when the material is exposed to outdoor conditions and, thus, fungal decay. Since moisture is the primary vector for fungal decay, accurate moisture prediction is a key component in service life assessment. For this purpose, the present study leverages existing measured data for linear regression of in-field moisture conditions of different wood species against climate parameters. Predictors of precipitation, relative humidity, and temperature were used in a finite distributed lag model to account for present and previous weather records. Issues of collinearity were addressed by ridge regression. The resulting model was, in general, able to describe the important features of different wood species. However, large errors were observed in certain periods, and it was hypothesized that these were related to thawing. Nevertheless, the results encourage additional effort into data-driven modelling of moisture content from measured data, and it is believed that non-linear models such as random forests and neural networks will be able to describe additional features and, in doing so, reduce the error. The study contributes to the ongoing efforts in developing effective, user-friendly, and open-source tools for performance-based service life assessment of wood. By improving our understanding of moisture content prediction in different softwoods, this research aims to enhance the reliability and sustainability of wood as a construction material. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8c028dc8-3d2d-4f60-a3ac-64a6c0bab2a0
- author
- Niklewski, Jonas LU ; van Niekerk, Philip Bester ; Meyer-Veltrup, Linda ; Sandak, Jakub and Brischke, Christian
- organization
- publishing date
- 2024-05-19
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Moisture, linear regression, wood, species, precipitation, decay, distributed lag
- host publication
- IRG55 Scientific Conference on Wood Protection : Knoxville, Tennessee, USA, 19 - 23 May, 2024
- series title
- Proceedings IRG Annual Meeting
- volume
- 2024
- article number
- IRG/WP 24-41005
- publisher
- International research group on wood protection
- conference name
- 55th Annual meeting of the IRGWP
- conference location
- Knoxville, United States
- conference dates
- 2024-05-19 - 2024-05-23
- ISSN
- 2000-8953
- language
- English
- LU publication?
- yes
- id
- 8c028dc8-3d2d-4f60-a3ac-64a6c0bab2a0
- date added to LUP
- 2025-03-19 22:06:16
- date last changed
- 2025-04-04 14:14:08
@inproceedings{8c028dc8-3d2d-4f60-a3ac-64a6c0bab2a0,
abstract = {{Sustainable use of wood requires an understanding of expected service life, particularly when the material is exposed to outdoor conditions and, thus, fungal decay. Since moisture is the primary vector for fungal decay, accurate moisture prediction is a key component in service life assessment. For this purpose, the present study leverages existing measured data for linear regression of in-field moisture conditions of different wood species against climate parameters. Predictors of precipitation, relative humidity, and temperature were used in a finite distributed lag model to account for present and previous weather records. Issues of collinearity were addressed by ridge regression. The resulting model was, in general, able to describe the important features of different wood species. However, large errors were observed in certain periods, and it was hypothesized that these were related to thawing. Nevertheless, the results encourage additional effort into data-driven modelling of moisture content from measured data, and it is believed that non-linear models such as random forests and neural networks will be able to describe additional features and, in doing so, reduce the error. The study contributes to the ongoing efforts in developing effective, user-friendly, and open-source tools for performance-based service life assessment of wood. By improving our understanding of moisture content prediction in different softwoods, this research aims to enhance the reliability and sustainability of wood as a construction material.}},
author = {{Niklewski, Jonas and van Niekerk, Philip Bester and Meyer-Veltrup, Linda and Sandak, Jakub and Brischke, Christian}},
booktitle = {{IRG55 Scientific Conference on Wood Protection : Knoxville, Tennessee, USA, 19 - 23 May, 2024}},
issn = {{2000-8953}},
keywords = {{Moisture; linear regression; wood; species; precipitation; decay; distributed lag}},
language = {{eng}},
month = {{05}},
publisher = {{International research group on wood protection}},
series = {{Proceedings IRG Annual Meeting}},
title = {{Modelling wood moisture content in outdoor conditions from measured data}},
url = {{https://lup.lub.lu.se/search/files/211748422/IRG55Niklewski_.pdf}},
volume = {{2024}},
year = {{2024}},
}