Predicting mechanical degradation indicators of silver fir wooden strips using near infrared spectroscopy
Predicting mechanical degradation indicators of silver fir wooden strips using near infrared spectroscopy
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DOI：
10.1007/s00107-017-1209-4
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发表时间：
2017-06
影响因子：
2.6
通讯作者：
J. Barré;F. Bourrier;L. Cécillon;L. Brancheriau;D. Bertrand;M. Thévenon;F. Rey
中科院分区：
文献类型：
--
作者：
J. Barré;F. Bourrier;L. Cécillon;L. Brancheriau;D. Bertrand;M. Thévenon;F. Rey
对构成木结构的生态工程结构的管理需要定期评估，包括木结构组成部分的腐烂程度。有一些方法可以在实验室或野外测量腐烂程度。然而，它们很少适用于生态工程结构的条件，或给出部分信息。本研究的目的是利用近红外光谱(NIRS)技术预测银杉木条在微生物分解过程中的两个机械降解指标(和)。对埋在法国格勒诺布尔附近(海拔200米)温室中的180根30 mm宽、500 mm长的木条的退化进行了1.5年的监测，并根据木结构设计中经典使用的两种力学性能--弹性模数(MOE)和断裂模数(MOR)的归一化损失值进行了设定。选取109个样本作为校正样本，利用偏最小二乘回归建立了两个独立的预测模型。基于近红外光谱的模型应用于47个样本的验证集，显示了良好的预测性能。模型的预测均方误差(RMSEP)为0.15，决定系数()为0.79。Themdel的RMSEP为0.13，-值为0.91。这些结果突显了近红外光谱在评估原木腐烂程度方面的巨大潜力。
The management of ecological engineering structures making up a timber structure requires periodical evaluations, including the level of decay of the constituent parts of the timber structure. Methods exist to measure the level of decay in the laboratory or in the field. However, they are rarely suitable for the conditions of ecological engineering structures, or give partial information. The aim of this study was to predict two mechanical degradation indicators (and) of silver fir (Abies alba) wooden strips during microbial decomposition using near-infrared spectroscopy (NIRS). For 1.5 years, the degradation of 180 squared wooden strips, 30 mm wide and 500 mm long, buried in a greenhouse near Grenoble, France (Altitude: 200 m) was monitored.andwere set from the normalized losses in modulus of elasticity (MOE) and modulus of rupture (MOR), two mechanical properties classically used for timber-structure design. A calibration set of 109 samples was selected to build two separate predictive models ofandusing partial least square regression. The NIR-based models applied to a validation set of 47 samples indicated good prediction performance. The model has a root mean square error of prediction (RMSEP) of 0.15 and a coefficient of determination () of 0.79. Themodel has a RMSEP of 0.13 and a-value of 0.91. These results highlight the considerable potential of NIRS in assessing the extension of decay in wooden logs.