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Machine learning-based prediction of surface checks and bending properties in weathered thermally modified timber
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- Van Blokland, Joran (author)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Skogens biomaterial och teknologi,Department of Forest Biomaterials and Technology
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- Adamopoulos, Stergios (author)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Skogens biomaterial och teknologi,Department of Forest Biomaterials and Technology
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(creator_code:org_t)
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- Elsevier BV, 2021
- 2021
- English.
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In: Construction and Building Materials. - : Elsevier BV. - 0950-0618. ; 307
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Abstract
Subject headings
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- 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.
Subject headings
- LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske -- Trävetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries -- Wood Science (hsv//eng)
Publication and Content Type
- ref (subject category)
- art (subject category)
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