Flexural and visual characteristics of fibre-managed plantation Eucalyptus globulus timber

被引:12
作者
Derikvand, Mohammad [1 ]
Kotlarewski, Nathan [1 ]
Lee, Michael [2 ]
Jiao, Hui [3 ]
Nolan, Gregory [1 ,2 ]
机构
[1] Univ Tasmania, Ctr Forest Value, Australian Res Council, Launceston, Tas 7250, Australia
[2] Univ Tasmania, Ctr Sustainable Architecture Wood CSAW, Launceston, Tas, Australia
[3] Univ Tasmania, Coll Sci & Engn, AMC, Sch Engn, Hobart, Tas, Australia
基金
澳大利亚研究理事会;
关键词
Plantation Eucalypt; timber processing; bending test; acoustic wave velocity; artificial neural network; non-destructive testing; ARTIFICIAL NEURAL-NETWORK; STIFFNESS; LOGS; WOOD; ELASTICITY; STRENGTH; MODULUS; PREDICTION; SELECTION; VELOCITY;
D O I
10.1080/17480272.2018.1542618
中图分类号
TB3 [工程材料学]; TS [轻工业、手工业、生活服务业];
学科分类号
0805 ; 080502 ; 0822 ;
摘要
The main goal of this study was to investigate the visual characteristics, recovery rate, and flexural properties of sawn boards from a fibre-managed plantation Eucalyptus globulus resource as a potential raw material for structural building applications. The impacts of the visual characteristics, strength-reducing features, and variation in basic density and moisture content on the bending modulus of elasticity (MOE) and modulus of rupture (MOR) of the boards were investigated. The reliabilities of different non-destructive methods in predicting MOE and MOR of the boards were evaluated, including log acoustic wave velocity measurement and numerical modellings. The MOE and MOR of the boards were significantly affected by the slope of grain, percentage of clear wood, and total number of knots in the loading zone of the boards. The normal variation in basic density significantly influenced the MOE of the boards while its effect on the MOR was insignificant. The numerical models developed using the artificial neural network (ANN) showed better accuracies in predicting the MOE and MOR of the boards than traditional multi-regression modelling and log acoustic wave velocity measurement. The ANN models developed in this study showed more than 78.5% and 79.9% success in predicting the adjusted MOE and MOR of the boards, respectively.
引用
收藏
页码:172 / 181
页数:10
相关论文
共 32 条
[1]  
[Anonymous], 1995, S AFRICAN FOR J, DOI DOI 10.1080/00382167.1995.9629877
[2]  
AS 2878, 2000, 2878 AS
[3]   Predictive Performance of Artificial Neural Network and Multiple Linear Regression Models in Predicting Adhesive Bonding Strength of Wood [J].
Bardak, S. ;
Tiryaki, S. ;
Bardak, T. ;
Aydin, A. .
STRENGTH OF MATERIALS, 2016, 48 (06) :811-824
[4]   Acoustic Wave Velocity as a Selection Trait in Eucalyptus nitens [J].
Blackburn, David ;
Hamilton, Matthew ;
Williams, Dean ;
Harwood, Chris ;
Potts, Brad .
FORESTS, 2014, 5 (04) :744-762
[5]   Stiffness and checking of Eucalyptus nitens sawn boards: genetic variation and potential for genetic improvement [J].
Blackburn, David ;
Hamilton, Matthew ;
Harwood, Chris ;
Innes, Trevor ;
Potts, Brad ;
Williams, Dean .
TREE GENETICS & GENOMES, 2010, 6 (05) :757-765
[6]  
Connell M.J., 2003, LOG PRESENTATION LOG, P62
[7]   Property relationships between spruce logs and structural timber [J].
Denzler, Julia K. ;
Weidenhiller, Andreas ;
Golser, Michael .
SCANDINAVIAN JOURNAL OF FOREST RESEARCH, 2015, 30 (07) :617-623
[8]   Visual stress grading of fibre-managed plantation Eucalypt timber for structural building applications [J].
Derikvand, Mohammad ;
Kotlarewski, Nathan ;
Lee, Michael ;
Jiao, Hui ;
Chan, Andrew ;
Nolan, Gregory .
CONSTRUCTION AND BUILDING MATERIALS, 2018, 167 :688-699
[9]  
Derikvand M, 2017, BIORESOURCES, V12, P4
[10]  
Edlund J, 2006, HOLZ ROH WERKST, V64, P273, DOI 10.1007/s00107-005-0091-7