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Probabilistic Models for the Tensile Properties of Split Boards and Their Application for the Prediction of Bending Properties of Engineered Timber Products Made of Norway Spruce

Author(s):
ORCID
Medium: journal article
Language(s): English
Published in: Buildings, , n. 8, v. 12
Page(s): 1143
DOI: 10.3390/buildings12081143
Abstract:

The main strength and elastic properties of structural timber products, such as glued laminated timber (glulam; GLT) and cross-laminated timber (CLT), are usually described via load-bearing models, which use the tensile properties parallel to the grain of the base material boards and finger joints as input parameters. These load-bearing models assume that the strength-graded boards will retain their full dimensions in the final product. In some applications or use cases, however, the structural timber products are split lengthwise, e.g., split/resawn glulam, or comprise a random share of in width randomly lengthwise split lamellas. As a result of splitting, the material properties assigned to these boards during the grading process in their full cross-sections are no longer valid. Examples of such structural timber products are the novel flex_GLT-beams which are cut out from large dimensional multi-laminated timber panels. In the following paper, the bending properties and system effects of resawn glulam and flex_GLT-beams are described by means of a 3D stochastic-numerical beam model that uses probabilistic models to create the input values for unsplit and split boards as well as finger joints. The models are successfully validated by our own tests and tests from literature and applied in numerous parameter studies.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

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  • About this
    data sheet
  • Reference-ID
    10688441
  • Published on:
    13/08/2022
  • Last updated on:
    10/11/2022
 
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