Tuyển tập các báo cáo nghiên cứu về lâm nghiệp được đăng trên tạp chí lâm nghiệp quốc tế, đề tài:" Prevision of the bending strength of timber with multivariate statistical approach. | Ann Sci For 1996 53 885-898 Elsevier INRA 885 Original article Prevision of the bending strength of timber with a multivariate statistical approach p Castéra1 c Faye1 A El Ouadrani2 1 Laboratoire de rhéologie du bois de Bordeaux CNRS lnra université Bordeaux-1 domaine de Hermitage 33610 Cestas zCentre technique du bois et de I ameublement CTBA 10 avenue Saint-Mandé 75012 Paris France Received 1 December 1994 accepted 17 November 1995 Summary - This paper describes a multivariate analysis applied on maritime pine lumber properties leading to a statistical modeling of the bending strength of this material using indirect nondestructive information. Correlation models are used in this study to estimate the distributional characteristics of concomitent properties that must be specified in European strength classes of lumber prEN338 mainly density modulus of elasticity MOE and modulus of rupture MOR . The basic steps of research are described as follows i Develop the statistical multivariate model using the empirical distributions and correlation matrix obtained from a database ii Simulate the distributions of MOE and MOR with incomplete information on density using Monte Carlo simulations to generate random variables. Hi Grade the simulated population according to prEN standards with one nondestructive estimator NDE and estimate the characteristics of each class. Comparison between simulated and experimental histograms of MOE and MOR indicates that the prediction of the first moments of distributions mean and standard deviation is better than the prediction of the lower tails. Simulations of grading using density or MOE as NDE have been made for different populations of beams. It is found that a slight increase in average density increases significantly the percentage of simulated high grade lumber C30 . Finally the comparison between simulated results and a visual grading according to the NFB 52-001 standard clearly shows the efficiency of statistical models for .
