Moisture prediction of timber for durability applications using data-driven modelling
(2023) World Conference on Timber Engineering (WCTE 2023) p.3808-3815- Abstract
- Durability and service life assessment is a major challenge for the design and use of timber in outdoor weather exposed environments. Rate of deterioration by fungal decay is closely linked to variations in wood moisture content. The objective of the present paper is to test and evaluate different data-driven models based on the multilinear regression (MLR) and artificial neural network (ANN) approach. Moisture content was predicted at the surface and core of a rain-exposed wooden element in the context of durability and service life assessment. Synthetic data stemming from a numerical model were used to fit time-series weather variables, including different combinations of time-lagged daily precipitation, relative humidity, and... (More)
- Durability and service life assessment is a major challenge for the design and use of timber in outdoor weather exposed environments. Rate of deterioration by fungal decay is closely linked to variations in wood moisture content. The objective of the present paper is to test and evaluate different data-driven models based on the multilinear regression (MLR) and artificial neural network (ANN) approach. Moisture content was predicted at the surface and core of a rain-exposed wooden element in the context of durability and service life assessment. Synthetic data stemming from a numerical model were used to fit time-series weather variables, including different combinations of time-lagged daily precipitation, relative humidity, and temperature, to temporal variations of daily average wood moisture content. Based on a set of statistical and qualitative analyses, using the weather variables lagged by 0 – 11 days as input variables for 11 mm depth moisture prediction, ANN showed the highest accuracy and least sensitivity to its initial setups, and could significantly outperform the MLR with the same input variables. The resulting models for surface and core moisture prediction were then tested against two different datasets consisting of measured data from wood specimens subjected to outdoor exposure. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3876481c-89d8-47bb-9c18-7ae630fd565e
- author
- Hosseini, Seyyed Hasan LU ; Niklewski, Jonas LU and van Niekerk, Philip Bester
- organization
- publishing date
- 2023-06
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Precipitation, Weather, Moisture, Artificial neural network (ANN), Timber, Durability
- host publication
- World Conference on Timber Engineering 2023 (WCTE 2023)
- article number
- 069179-0495
- pages
- 8 pages
- publisher
- World Conference on Timber Engineering 2023
- conference name
- World Conference on Timber Engineering (WCTE 2023)
- conference location
- Olso, Norway
- conference dates
- 2023-06-19 - 2023-06-22
- external identifiers
-
- scopus:85171803817
- ISBN
- 9781713873273
- DOI
- 10.52202/069179-0495
- language
- English
- LU publication?
- yes
- id
- 3876481c-89d8-47bb-9c18-7ae630fd565e
- date added to LUP
- 2023-07-28 10:57:44
- date last changed
- 2023-10-20 04:00:56
@inproceedings{3876481c-89d8-47bb-9c18-7ae630fd565e,
abstract = {{Durability and service life assessment is a major challenge for the design and use of timber in outdoor weather exposed environments. Rate of deterioration by fungal decay is closely linked to variations in wood moisture content. The objective of the present paper is to test and evaluate different data-driven models based on the multilinear regression (MLR) and artificial neural network (ANN) approach. Moisture content was predicted at the surface and core of a rain-exposed wooden element in the context of durability and service life assessment. Synthetic data stemming from a numerical model were used to fit time-series weather variables, including different combinations of time-lagged daily precipitation, relative humidity, and temperature, to temporal variations of daily average wood moisture content. Based on a set of statistical and qualitative analyses, using the weather variables lagged by 0 – 11 days as input variables for 11 mm depth moisture prediction, ANN showed the highest accuracy and least sensitivity to its initial setups, and could significantly outperform the MLR with the same input variables. The resulting models for surface and core moisture prediction were then tested against two different datasets consisting of measured data from wood specimens subjected to outdoor exposure.}},
author = {{Hosseini, Seyyed Hasan and Niklewski, Jonas and van Niekerk, Philip Bester}},
booktitle = {{World Conference on Timber Engineering 2023 (WCTE 2023)}},
isbn = {{9781713873273}},
keywords = {{Precipitation; Weather; Moisture; Artificial neural network (ANN); Timber; Durability}},
language = {{eng}},
pages = {{3808--3815}},
publisher = {{World Conference on Timber Engineering 2023}},
title = {{Moisture prediction of timber for durability applications using data-driven modelling}},
url = {{http://dx.doi.org/10.52202/069179-0495}},
doi = {{10.52202/069179-0495}},
year = {{2023}},
}