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Abstract

While the traditional approach to engineering materials aims to reduce their variability through a (generally energy-intensive) refining process, a more environmentally appropriate approach is to keep the product as natural as possible and quantify their variability, while reducing their variability only as necessary. This is the approach for sawn lumber and other solid wood products, made possible by the application of advanced statistical theory in the manufacturing, grading, and evaluation processes and in the engineering design models. This article reviews a number of statistical advances related to these objectives, and it ends with a view of a future characterized by engineered wood products and the use of high technology.

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2018-03-07
2024-05-07
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Literature Cited

  1. Amorim SD, Johnson RA. 1986. Experimental designs for estimating the correlation between two destructively tested variables. J. Am. Stat. Assoc. 81:807–12 [Google Scholar]
  2. ASTM. 2007. Standard practice for establishing allowable properties for visually-graded dimension lumber from in-grade tests of full-size specimens. Tech. Rep. ASTM D1990-16, ASTM Intl., West Conshohocken, PA. http://dx.doi.org/10.1520/D1990-07 [Crossref]
  3. Buckingham E. 1914. On physically similar systems; illustrations of the use of dimensional equations. Phys. Rev. 4:345–76 [Google Scholar]
  4. Cai S. 2014. On dual empirical likelihood inference under semiparametric density ratio models in the presence of multiple samples with applications to long term monitoring of lumber quality PhD Thesis, Univ. B. C.
  5. Cai S, Chen J, Zidek JV. 2017. Hypothesis testing in the presence of multiple samples under density ratio models. Stat. Sin. 27:761–83 [Google Scholar]
  6. Cai Y. 2015. Statistical methods for relating strength properties of dimensional lumber PhD Thesis, Univ. B. C.
  7. Castéra P, Faye C, El Ouadrani A. 1996. Prevision of the bending strength of timber with a multivariate statistical approach. Ann. Sci. For. 53:885–96 [Google Scholar]
  8. Chen J, Liu Y. 2013. Quantile and quantile-function estimations under density ratio model. Ann. Stat. 41:1669–92 [Google Scholar]
  9. Chen J, Li P, Liu Y, Zidek JV. 2017. Monitoring test under nonparametric random effects model. arXiv1610.05809 [stat.ME]
  10. Cheng Y. 2010. Wood property relationships and survival models in reliability Master's Thesis, Univ. B. C.
  11. Cheng Y, Wu L, Lum C, Zidek J, Yu T. 2016. Wood property relationships and survival models in reliability. Appl. Stochast. Models Bus. Ind. 32:792–803 [Google Scholar]
  12. Evans J, Johnson R, Green D. 1984. Estimating the correlation between variables under destructive testing, or how to break the same board twice. Technometrics 26:285–90 [Google Scholar]
  13. Foschi R. 1979. A discussion on the application of the safety index concept to wood structures. Can. J. Civ. Eng. 6:51–58 [Google Scholar]
  14. Foschi RO, Barrett JD. 1982. Load-duration effects in western hemlock lumber. J. Struct. Div. 108:1494–510 [Google Scholar]
  15. Foschi RO, Folz B, Yao F. 1989. Reliability-Based Design of Wood Structures Vancouver: Univ. B. C. Press
  16. Foschi RO, Yao FZ. 1986. Another look at three duration of load models. CIB/W18 timber structures. Proc. IUFRO Wood Eng. Group Meet., Florence, Italy Pap. 19-9-1
  17. FPL (For. Prod. Lab.). 2010. Wood handbook—wood as an engineering material Tech. Rep. FPL-GTR-190, For. Prod. Lab., Dep. Agric., For. Ser., Madison, WI
  18. Green DW, Evans JW, Johnson RA. 1984. Investigating of the procedure for estimating concomitance of lumber strength properties. Wood Fiber Sci 16:427–40 [Google Scholar]
  19. Hietaniemi R, López MB, Hannuksela J, Silvén O. 2014. A real-time imaging system for lumber strength prediction. For. Prod. J. 64:126–33 [Google Scholar]
  20. Hoffmeyer P, Sørensen JD. 2007. Duration of load revisited. Wood Sci. Technol. 41:687–711 [Google Scholar]
  21. Johnson RA, Evans J, Green D. 1999. Nonparametric Bayesian predictive distributions for future order statistics. Stat. Probab. Lett. 41:247–54 [Google Scholar]
  22. Johnson RA, Galligan WL. 1983. Estimating the concomitance of lumber strength properties. Wood Fiber Sci 15:235–44 [Google Scholar]
  23. Keziou A, Leoni-Aubin S. 2008. On empirical likelihood for semiparametric two–sample density ratio models. J. Stat. Plann. Inference 138:915–28 [Google Scholar]
  24. Köhler J, Svensson S. 2002. Probabilistic modeling of duration of load effects in timber structures. Proc. 35th Meet. Int. Counc. Res. Innov. Build. Constr. Work. Comm. W18 Timber Struct. CIB-W18 Pap. 35-17 [Google Scholar]
  25. Kondo Y, Zidek JV. 2013. Bayesian nonparametric subset selection procedures with Weibull components. Tech. Rep. 273, Dep. Stat., Univ. B. C.
  26. Kretschmann DE. 2010. Stress grades and design properties for lumber, round timber, and ties. See FPL 2010 71–16
  27. Kretschmann DE, Evans JW, Brown L. et al. 1999. Monitoring of visually graded structural lumber. Res. Pap. FPL-RP-576. US Dep. Agric., For. Serv., For. Prod. Lab Madison, WI:
  28. Madsen B. 1975. Strength values for wood and limit states design. Can. J. Civ. Eng. 2:270–79 [Google Scholar]
  29. Nijman T, Verbeek M, van Soest A. 1991. The efficiency of rotating-panel designs in an analysis-of-variance model. J. Econom. 49:373–99 [Google Scholar]
  30. Olsson A, Oscarsson J, Serrano E, Källsner B, Johansson M, Enquist B. 2013. Prediction of timber bending strength and in-member cross-sectional stiffness variation on the basis of local wood fibre orientation. Eur. J. Wood Wood Prod. 71:319–33 [Google Scholar]
  31. Rizvi M, Sobel M. 1967. Nonparametric procedures for selecting a subset containing the population with the largest α-quantile. Ann. Math. Stat. 38:1788–803 [Google Scholar]
  32. Van Eeden C, Zidek JV. 2012. Subset selection–extended Rizvi-Sobel for unequal sample sizes and its implementation. J. Nonparametric Stat. 24:299–315 [Google Scholar]
  33. Wong SW, Lum C, Wu L, Zidek JV. 2015. Quantifying uncertainty in lumber grading and strength prediction: a Bayesian approach. Technometrics 58:236–43 [Google Scholar]
  34. Wong SW, Zidek JV. 2017. Dimensional and statistical foundations for accumulated damage models. J. Wood Sci. Technol. In press
  35. Wood L. 1951. Relation of strength of wood to duration of stress Rep. R1916 US Dep. Agric., For. Serv., For. Prod. Lab., Madison, WI
  36. WWPA (West. Wood Prod. Assoc.). 2005. In-grade lumber testing Tech. Rep. 02 WWPA, Portland, OR
  37. Zhai Y. 2011. Dynamic duration of load models Master's Thesis, Univ. B. C.
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