Prediction of Tensile Modulus of Elasticity of Earlywood and Latewood from Hybrid Larch

HUANG He; YANG Chen; LI Zhu; XIANG Elin; JIANG Jiali; ZHOU Yongdong

doi:10.12326/j.2096-9694.2025086

EVALUATION OF WOOD PROPERTIES OF DIRECTIONALLY CULTIVATED PINE TREES | Views : 0 下载量: 39 CSCD: 0 CNKI被引量: 0 Crossref: 0 Wos: 0 Scopus: 0 更多指标

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Prediction of Tensile Modulus of Elasticity of Earlywood and Latewood from Hybrid Larch
Research Institute of Wood Industry，Chinese Academy of Forestry，Beijing 100091，China
Vol. 39, Issue 6, Pages: 26-32(2025)
DOI： 10.12326/j.2096-9694.2025086
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黄鹤,杨晨,李珠等.杂种落叶松早材和晚材拉伸弹性模量预测[J].木材科学与技术,2025,39(06):26-32. DOI： 10.12326/j.2096-9694.2025086.

HUANG He,YANG Chen,LI Zhu,et al.Prediction of Tensile Modulus of Elasticity of Earlywood and Latewood from Hybrid Larch[J].Chinese Journal of Wood Science and Technology,2025,39(06):26-32. DOI： 10.12326/j.2096-9694.2025086.

摘要

以5 a、14 a、29 a和40 a的杂种落叶松（

Larix kaempferi

×L. gmelinii

9）最外侧5个生长轮早材和晚材的组织切片为研究对象，分析气干密度与拉伸弹性模量的相关关系，并利用表格先验数据拟合网络（Tabular prior-data fitted network，TabPFN）模型对拉伸弹性模量进行建模与预测。结果表明：4个树龄早材和晚材的拉伸弹性模量与气干密度均呈现正相关，晚材的决定系数（

）高于早材，两者数据联合拟合的结果显著优于单独拟合，

介于0.94至0.97之间。基于TabPFN构建的模型可实现落叶松早材与晚材拉伸弹性模量的预测，预测值与实测值平均相对误差（mean absolute percentage error，MAPE）为16.03%。研究结果可为落叶松木材拉伸弹性模量预测提供参考，为木材材质的快速评估提供理论依据。

Abstract

In this research

earlywood (EW) and latewood (LW) tissue slices were taken from the outermost five growth rings of hybrid larch (

Larix kaempferi

×L. gmelinii

9) at 5

and 40 years with an aim at revealing the variation of air-dry density and tensile modulus of elasticity (MOE)

as well as their correlation. Furth

ermore

a tabular prior-data fitted network (TabPFN) model was employed to simulate and predict tensile MOE. Results showed that

at any given tree age

a positive correlation was observed between the tensile MOE and air-dry density for both EW and LW

with the coefficients of determination (

²) being higher for LW than for EW. The combined fitting of EW and LW data yielded significantly better results than individual fittings

with

ranging from 0.94 to 0.97. A prediction model constructed based on TabPFN deep learning enabled the prediction of tensile MOE of EW and LW in hybrid larch

with a mean absolute percentage error (MAPE) of 16.03% between predicted and experimental values. These findings could support the prediction of tensile MOE of larch wood for rapidly evaluating wood quality.

关键词

Keywords

references

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