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.
作者
黄鹤
杨晨
李珠
向娥琳
蒋佳荔
周永东
HUANG He;YANG Chen;LI Zhu;XIANG Elin;JIANG Jiali;ZHOU Yongdong(Research Institute of Wood Industry,Chinese Academy of Forestry,Beijing 100091,China)
出处
《木材科学与技术》
北大核心
2025年第6期26-32,共7页
Chinese Journal of Wood Science and Technology
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)。
