Tensile strength grading of beech (Fagus sylvatica L.) lamellas from multiple origins, cross sections and qualities

Maximilian Westermayr, Monika Zeilhofer, Andreas Rais, Andriy Kovryga, Jan Willem G. Van De Kuilen

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

1 Zitat (Scopus)

Abstract

The market share of European beech (Fagus sylvatica L.) wood in the construction sector is low despite an increase in beech stock in Central European Forests in recent years. More efficient sawing techniques, higher lamella grading yields and solving of adhesion challenges may increase the competitiveness of beech glulam and promote its use. The aim of this paper is to revise the lamella grading system in the current German technical approval for beech glulam Z-9.1-679:2019 (DIBt (2019). BS-Holz aus Buche und BS-Holz Buche Hybridträger und zugehörige Bauarten. Allgemeine bauaufsichtliche Zulassung Z-9.1-679:2019. Deutsches Institut für Bautechnik) and to suggest modifications in the lamella grading rules for glulam production allowing higher yields and reliable tensile strength values at the same time. The unique dataset in this study combined different origins of lamellas and covered a wide range of visual, physical and mechanical wood characteristics including a high amount of low quality material. Indicating properties (IPs) for tensile strength, such as knot parameters and dynamic modulus of elasticity, were contrasted with tensile strength and static modulus of elasticity. Beech lamellas, graded by means of Z-9.1-679:2019 (DIBt (2019). BS-Holz aus Buche und BS-Holz Buche Hybridträger und zugehörige Bauarten. Allgemeine bauaufsichtliche Zulassung Z-9.1-679:2019. Deutsches Institut für Bautechnik), did not achieve the tensile strengths required for glulam production in many grading classes and the yield was low. A machine grading approach with dynamic modulus of elasticity as a single grading criterion gave higher yields than the current grading procedure and high reliability for tensile strength prediction with a prediction accuracy of R 2 = 0.67.

OriginalspracheEnglisch
Seiten (von - bis)397-407
Seitenumfang11
FachzeitschriftHolzforschung
Jahrgang76
Ausgabenummer5
DOIs
PublikationsstatusVeröffentlicht - 1 Mai 2022

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