Nasir Vahid, Fathi Hamidreza, Fallah Arezoo, Kazemirad Siavash, Sassani Farrokh, Antov Petar
Department of Mechanical Engineering, The University of British Columbia (UBC), Vancouver, BC 2054-6250, Canada.
School of Mechanical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
Materials (Basel). 2021 Oct 22;14(21):6314. doi: 10.3390/ma14216314.
Color parameters were used in this study to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. A CIELAB color measuring system was employed to study the color changes in wood samples. The color parameters were fed into a decision tree model for predicting the MOE and MOR values of the wood samples. The results indicated a reduction in the mechanical properties of the samples, where fir and alder were the most and least degraded wood under weathering conditions, respectively. The mechanical degradation was correlated with the color change, where the most resistant wood to color change exhibited less reduction in the mechanical properties. The predictive machine learning model estimated the MOE and MOR values with a maximum R of 0.87 and 0.88, respectively. Thus, variations in the color parameters of wood can be considered informative features linked to the mechanical properties of small-sized and clear wood. Further research could study the effectiveness of the model when analyzing large-sized timber.
本研究使用颜色参数来开发一个机器学习模型，用于预测人工老化的杉木、桤木、橡木和杨木的力学性能。采用CIELAB颜色测量系统来研究木材样本的颜色变化。将颜色参数输入决策树模型，以预测木材样本的弹性模量（MOE）和抗弯强度（MOR）值。结果表明，样本的力学性能有所下降，其中杉木是在风化条件下退化最严重的木材，桤木则是退化最少的木材。力学性能的退化与颜色变化相关，其中最耐颜色变化的木材在力学性能上的降低幅度较小。预测性机器学习模型估计的弹性模量和抗弯强度值的最大相关系数（R）分别为0.87和0.88。因此，木材颜色参数的变化可被视为与小尺寸无缺陷木材力学性能相关的信息特征。进一步的研究可以探讨该模型在分析大尺寸木材时的有效性。
