当前位置: X-MOL 学术Wood Mater. Sci. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Analytical models of the mechanical properties of Japanese larch (Larix kaempferi (Lamb.) Carr.) based on non-destructive testing and visual grading parameters
Wood Material Science & Engineering ( IF 2.2 ) Pub Date : 2019-06-11 , DOI: 10.1080/17480272.2019.1626481
M. J. Barriola 1 , J. R. Aira 2 , J. L. Villanueva 3
Affiliation  

ABSTRACT

Increasing concern about the rapid proliferation of fungi attacking stands of radiata pine in the north of the Iberian Peninsula has led to a search for alternative conifer species that would be able to replace the affected trees. In this context one of the candidate species is the Japanese larch, a species that has not been structurally characterised within Spanish standards. This study describes several analytical models which make it possible to estimate the strength and rigidity variables of this species, using a combination of non-destructive testing techniques and visual grading parameters. Models were obtained by multiple regressions to estimate the global elasticity modulus (MOEG) and the local elasticity modulus (MOE), including and excluding correction for moisture content. The modulus of rupture (MOR) was also calculated while including and excluding correction for size. When the analytical models were subjected to the relevant corrections they were all less accurate. Estimation of the MOEG (r 2 = 0.66) was slightly more accurate than estimation of the MOE (r 2 = 0.56), and it was far more accurate than estimation of the MOR (r 2 = 0.44).



中文翻译:

基于无损检测和视觉分级参数的日本落叶松(Larix kaempferi(Lamb。)Carr。)力学性能的分析模型

抽象的

对伊比利亚半岛北部辐射松的真菌侵袭林分迅速扩散的担忧日益引起人们的关注,导致人们寻求其他能够替代受影响树木的针叶树种。在这种情况下,候选树种之一是日本落叶松,该树种尚未在西班牙标准内进行结构鉴定。这项研究描述了几种分析模型,这些模型可以结合使用非破坏性测试技术和视觉分级参数来估算该物种的强度和刚度变量。通过多次回归获得模型,以估算整体弹性模量(MOEG)和局部弹性模量(MOE),包括但不包括水分含量的校正。还计算了断裂模量(MOR),同时包括但不包括尺寸校正。当对分析模型进行相关的校正时,它们的准确性都会降低。MOEG的估计(r 2  = 0.66)比MOE估计(r 2  = 0.56)更准确,并且比MOR估计(r 2  = 0.44)更准确。

更新日期:2019-06-11
down
wechat
bug