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杂种落叶松早材和晚材拉伸弹性模量预测    

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

文献类型:期刊文献

中文题名:杂种落叶松早材和晚材拉伸弹性模量预测

英文题名:Prediction of Tensile Modulus of Elasticity of Earlywood and Latewood from Hybrid Larch

作者:黄鹤[1] 杨晨[1] 李珠[1] 向娥琳[1] 蒋佳荔[1] 周永东[1]

第一作者:黄鹤

机构:[1]中国林业科学研究院木材工业研究所,北京100091

年份:2025

卷号:39

期号:6

起止页码:26-32

中文期刊名:木材科学与技术

外文期刊名:Chinese Journal of Wood Science and Technology

收录:;北大核心:【北大核心2023】;

基金:农业生物育种国家科技重大专项课题“纸浆材及结构材用松树木材性质与质量评价”(2023ZD0405905)。

语种:中文

中文关键词:杂种落叶松;气干密度;拉伸弹性模量;TabPFN模型

外文关键词:hybrid larch;air-dry density;tensile modulus of elasticity;TabPFN model

分类号:S781.33;S791.22

摘要:以5 a、14 a、29 a和40 a的杂种落叶松(Larix kaempferi 5×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(R^(2))高于早材,两者数据联合拟合的结果显著优于单独拟合,R^(2)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。
In this research,earlywood(EW)and latewood(LW)tissue slices were taken from the outermost five growth rings of hybrid larch(Larix kaempferi 5×L.gmelinii 9)at 5,14,29,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.Furthermore,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(R^(2))being higher for LW than for EW.The combined fitting of EW and LW data yielded significantly better results than individual fittings,with R^(2) 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.

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