Machine learning-based prediction of internal checks in weathered thermally modified timber

Sammanfattning

This study investigated possibilities to predict the presence of internal checks in thermally modified Norway spruce timber after 2.5 years of weathering based on the initial properties of the boards. Machine-learning classification enabled sorting the input parameters based on their relative importance for accurate predictions. The parameters of thermally modified timber with the highest relative importance were annual ring width followed by initial moisture content, density and dynamic stiffness. Whereas after kiln drying these were, density, annual ring width, initial moisture content and acoustic velocity. The results showed that predictions are possible, and an accuracy of 67% was achieved by using annual ring width combined with density and initial moisture content, or acoustic velocity that can be determined after either kiln drying or thermal treatment. (C) 2020 Published by Elsevier Ltd.

Nyckelord

Acoustic velocity; Decision tree; Non-destructive testing; Norway spruce; Outdoor above-ground exposure; Timber grading

Publicerad i

Construction and Building Materials
2021, volym: 281, artikelnummer: 122193
Utgivare: ELSEVIER SCI LTD

SLU författare

• Adamopoulos, Stergios

  • Institutionen för skogens biomaterial och teknologi, Sveriges lantbruksuniversitet
    

Globala målen (SDG)

SDG12 Hållbar konsumtion och produktion

UKÄ forskningsämne

Trävetenskap

Publikationens identifierare

• DOI: https://doi.org/10.1016/j.conbuildmat.2020.122193

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/111566