This study proposes a combined machine learning–wave propagation approach for nondestructive prediction of the modulus of elasticity (MOE) and rupture (MOR) of timber subjected to ultraviolet (UV) radiation. Fir, poplar, alder, and oak wood specimens were subjected to artificial UV aging and assessed using the Lamb wave propagation. Different features including the wave characteristics and the viscoelastic properties of the specimens were obtained from the Lamb wave propagation tests. The extracted features trained a decision tree model for MOE and MOR prediction. The UV radiation caused a decrease in the elastic properties of wood but increased its viscoelasticity. The results also showed a decrease in the wave velocity and an increase in the wave amplitude decay with the UV exposure time. It was revealed that compared with the wave velocity, the wave amplitude decay was better correlated to the MOE of MOR of UV-degraded wood. The MOE and MOR of UV-degraded wood were accurately predicted by the machine learning models fed by the features extracted from the Lamb wave propagation tests, where the shear storage modulus was found as the most important feature for training the models. It was concluded that the proposed approach offers a great tool for in-situ monitoring of wood structures under weathering and photodegradation conditions.

这项研究提出了一种结合机器学习-波传播的方法，可以对遭受紫外线（UV）辐射的木材的弹性模量（MOE）和断裂强度（MOR）进行无损预测。杉木，白杨，al木和栎木标本经过人工紫外线老化，并使用兰姆波传播进行评估。从兰姆波传播测试获得了包括波特性和样品的粘弹性的不同特征。提取的特征训练了用于MOE和MOR预测的决策树模型。紫外线会降低木材的弹性，但会增加其粘弹性。结果还表明，随着紫外线暴露时间的增加，波速降低，波幅衰减增加。结果表明，与波速相比，波幅衰减与紫外线降解木材的MOR的MOE更好地相关。机器学习模型准确地预测了紫外线降解木材的MOE和MOR，这些模型是根据从Lamb波传播测试中提取的特征得出的，其中剪切储能模量是训练模型的最重要特征。结论是，提出的方法为风化和光降解条件下的木结构现场监测提供了一个很好的工具。