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Prediction of tensile strength of sawn timber: models for calculation of yield in strength classes
Linnaeus University, Faculty of Technology, Department of Building Technology.ORCID iD: 0000-0003-4518-570X
Linnaeus University, Faculty of Technology, Department of Building Technology.ORCID iD: 0000-0002-6410-1017
Linnaeus University, Faculty of Technology, Department of Building Technology.ORCID iD: 0000-0002-8513-0394
2020 (English)In: Materials and Structures, ISSN 1359-5997, E-ISSN 1871-6873, Vol. 53, no 3, p. 1-15, article id 55Article in journal (Refereed) Published
Abstract [en]

In Europe, strength classes for structural timber and glulam lamellae are defined by minimum requirements of characteristic values of the grade determining properties (GDPs). To fulfill these minimum requirements of characteristic values in the daily production at sawmills, indicating properties (IPs) to GDPs are calculated for each board and based on predetermined limits of the IPs (settings) boards are assigned to the graded class, or rejected. The aims of this paper is to address and discuss two different grading procedures/models that can be applied when settings for IPs that reflects a local board property are derived and to show how the yield in different T-classes depend on the model applied. It is not always that a board’s weakest cross-section is evaluated in a destructive test. An IP representing a local board property can therefore be determined either as the lowest property of the tested part of the board or as the lowest property along the whole board when applied to derive settings. Results presented in this paper show that too low settings and too large yields are obtained when the latter IP is employed. Similarly, IPs reflecting a global board property, like axial dynamic MOE, also give too low settings and too high yield in strength classes. This paper is the second and closing part of a series of two paper on prediction of GDPs and procedures for grading sawn timber into T-classes.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 53, no 3, p. 1-15, article id 55
Keywords [en]
Grading of timber, Laser scanning, Fibre direction, Dynamic modulus of elasticity, Norway spruce
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
URN: urn:nbn:se:lnu:diva-94131DOI: 10.1617/s11527-020-01485-wISI: 000534325800002Scopus ID: 2-s2.0-85084486817OAI: oai:DiVA.org:lnu-94131DiVA, id: diva2:1427913
Funder
Knowledge Foundation, 20150179Available from: 2020-05-04 Created: 2020-05-04 Last updated: 2021-05-11Bibliographically approved
In thesis
1. Modelling and strength grading of structural timber and glulam lamellae on the basis of optical scanning and dynamic excitation
Open this publication in new window or tab >>Modelling and strength grading of structural timber and glulam lamellae on the basis of optical scanning and dynamic excitation
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Machine strength grading of sawn timber is a sawmill process in which density, modulus of elasticity (MOE) and bending or tensile strength are predicted such that the timber can be assigned to strength classes. The predictions of these properties are performed using one or several so-called indicating properties (IPs), which represent a board property, or combination of board properties, measured non-destructively. A limitation of today’s strength grading is that the IPs applied in the industry for prediction of strength, in general, are based on rather weak statistical relationships between IPs and strength properties, which in turn results in poor material utilisation. It is well known that the strength of sawn timber is associated with the presence of knots and their surrounding fibre disorientations. Local fibre direction at surfaces of softwood can be determined by means of the light scattering that occur when a wood surface is illuminated by a dot-laser, i.e. by application of the so-called tracheid effect. Lately, IPs based on such measurements have been developed, and some of the suggested IPs have a strong statistical relationship to bending strength. The purposes of the research presented in this thesis are to contribute with knowledge of possibilities and limitations of the tracheid effect and of data of fibre directions in the vicinity of knots, to evaluate if information of fibre directions at surfaces of Norway spruce sawn timber can be used to achieve a better material utilisation of glulam lamellae and finger-jointed timber, and to provide insight regarding the grading regulations in Europe. Results presented herein show that knots and fibre direction within the interior of boards can be modelled on the basis of data obtained by means of the tracheid effect, but also that a previously proposed method to determine out-of-plane fibre angles gives poor accuracy. As regards grading of glulam lamellae, an IP based on fibre directions and dynamic MOE is proposed for prediction of tensile strength. The latter is used when grading glulam lamellae. Application of the proposed IP resulted in substantially increased yield in strength classes. It is also shown that this IP is applicable for boards with sawn as well as with planed surface finish. Regarding current regulations for machine strength grading in Europe, results indicate that grading based on global board properties give higher yield than what is appropriate.

Place, publisher, year, edition, pages
Växjö: Linnaeus University Press, 2020. p. 68
Series
Linnaeus University Dissertations ; 380/2020
Keywords
Fibre direction, finger joint, machine strength grading, knots, tracheid effect, Norway spruce
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-94126 (URN)978-91-89081-47-5 (ISBN)978-91-89081-48-2 (ISBN)
Public defence
2020-04-29, N1017, P G Vejdes väg, Växjö, 10:00 (English)
Opponent
Supervisors
Available from: 2020-05-04 Created: 2020-05-04 Last updated: 2024-02-26Bibliographically approved

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Briggert, AndreasOlsson, AndersOscarsson, Jan

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