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Machine learning-based prediction of surface checks and bending properties in weathered thermally modified timber

Adamopoulos, Stergios and Van Blokland, Joran and Nasir, Vahid and Cool, Julie (2021). Machine learning-based prediction of surface checks and bending properties in weathered thermally modified timber. Construction and Building Materials. 307 , 124996
[Research article]

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Abstract

Machine learning (ML)-based models, decision tree and ANFIS, were used to predict the degree of surface checking and bending properties of 30-month weathered thermally modified timber. The results showed that the investigated initial board properties did not allow accurate predictions of surface checks. ML regression and clustering analysis confirmed important variables for accurate predictions of bending properties were dynamic stiffness, acoustic velocity, density and lowest local bending modulus. ML models performed better than conventional regression models used for timber grading, and a prediction accuracy of 80–90% for bending stiffness and 50–70% for bending strength could be achieved.

Authors/Creators:Adamopoulos, Stergios and Van Blokland, Joran and Nasir, Vahid and Cool, Julie
Title:Machine learning-based prediction of surface checks and bending properties in weathered thermally modified timber
Series Name/Journal:Construction and Building Materials
Year of publishing :2021
Volume:307
Article number:124996
Number of Pages:11
ISSN:0950-0618
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Wood Science
Keywords:acoustic velocity, adaptive neuro-fuzzy inference system (ANFIS), decision tree, non-destructive testing, Norway spruce, outdoor above-ground exposure, timber grading, ThermoWood®
URN:NBN:urn:nbn:se:slu:epsilon-p-114060
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-114060
Additional ID:
Type of IDID
DOI10.1016/j.conbuildmat.2021.124996
ID Code:25920
Faculty:S - Faculty of Forest Sciences
Department:(S) > Department of Forest Biomaterials and Technology
Deposited By: SLUpub Connector
Deposited On:22 Oct 2021 07:25
Metadata Last Modified:22 Oct 2021 07:31

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