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Prediction of tensile strength of sawn timber: definitions and performance of indicating properties based on surface laser scanning and dynamic excitation
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. 1-20, article id 54Article in journal (Refereed) Published
Abstract [en]

The presence of wood irregularities such as knots are decisive for the mechanical properties of sawn timber, and efficient utilisation of timber requires methods by which grade determining properties can be predicted with high accuracy. In the glulam and sawmilling industries today, there is a potential and a need for more accurate prediction methods. This paper concerns the performance of a set of indicating properties calculated by means of data from surface laser scanning, dynamic excitation and X-ray scanning, the latter used to obtain boards’ average density. A total number of 967 boards of Norway spruce originating from Finland, Norway and Sweden were used to determine statistical relationships between the indicating properties and the grade determining properties used to grade sawn timber into T-classes. Results show that the indicating properties give coefficients of determination to tensile strength as high as 0.70. Furthermore, results also show that laser scanning of boards with sawn surface finish give basis for almost as accurate grading as what scanning of planed boards do. The results imply that more accurate grading of timber into T-classes is possible by application of a new set of indicating properties. This paper is part one of a series of two papers. In the second paper, two models to derive settings and calculate yield in different strength classes using the indicating properties presented herein are compared and discussed.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 53, no 3, p. 1-20, article id 54
Keywords [en]
Laser scanning, Fibre direction, Dynamic modulus of elasticity, Grading of timber, Norway spruce
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
URN: urn:nbn:se:lnu:diva-94130DOI: 10.1617/s11527-020-01460-5ISI: 000534325800001Scopus ID: 2-s2.0-85084519647OAI: oai:DiVA.org:lnu-94130DiVA, id: diva2:1427908
Funder
Knowledge Foundation, 20150179Available from: 2020-05-04 Created: 2020-05-04 Last updated: 2021-05-07Bibliographically 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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