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 | ||||
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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: |
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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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