Use of Longitudinal Vibration and Visual Characteristics to Predict Mechanical Properties of No. 2 Southern Pine 2 × 8 and 2 × 10 Lumber

Authors

  • F. J.N. Franca Mississippi State University http://orcid.org/0000-0001-7807-8877
  • T. S. F.A. França Mississippi State university
  • R. D. Seale Mississippi State University
  • R. Shmulsky Mississippi State University

Keywords:

knots, dimensional lumber, strength, stiffness, statistical models

Abstract

The objective of this study was to evaluate the accuracy of single MOE and MOR and combined mechanical properties with visual characteristics to improve the prediction of 2 x 8 and 2 x 10 No. @ southern pine lumber. This study evaluated the following variables: nondestructive tests, knots (knot diameter ratio [KDR] and knot area ratio), density, and mechanical properties (stiffness [MOE] and strength [MOR]). A total of 486 pieces were used, and linear regression models were constructed using stepwise selects to determine the best variables to estimate the MOE and MOR of southern pine lumber. The best single predictor for MOE and MOR was dynamic MOE (dMOE) followed by density. Among the two knot measurement methods used, the KDR best predicted stiffness and strength. For predicting the MOE, the variables dMOE, density, and KDR. The results showed that the addition of knot measurements to the models is able to improve the prediction of mechanical properties.

Author Biographies

F. J.N. Franca, Mississippi State University

Assistant Research Professor at Department of Sustainable Bioproducts, Mississippi State university 

T. S. F.A. França, Mississippi State university

Assistant Professor at Department of Sustainable Bioproducts, Mississippi State university

R. D. Seale, Mississippi State University

Warren S. Thompson Professor at Department of Sustainable Bioproducts, Mississippi State University

R. Shmulsky, Mississippi State University

Professor and Department Head at Department of Sustainable Bioproducts, Mississippi State University

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Published

2020-07-28

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Research Contributions