Identification of wood species and duration of exposure in weathered wood using guided wave propagation

Nasir, Vahid; Parvari, Yousef; Fathi, Hamidreza; Kazemirad, Siavash; Sassani, Farrokh

doi:10.1080/17480272.2022.2092771

Cited by 1 publication

(1 citation statement)

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“…Details of the CART model can be found elsewhere [ 57 , 58 ]. The CART algorithm was successfully used for check prediction in weathered thermally modified timber [ 59 ] and for characterization and classification of artificially weathered wood [ 60 , 61 , 62 ]. Ensemble learning based on bagging or boosting algorithms could be applied to reduce the variance of a single prediction by a tree using multiple weak learners (decision tree).…”

Section: Methodsmentioning

confidence: 99%

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

et al. 2023

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Monitoring the moisture content (MC) of wood and avoiding large MC variation is a crucial task as a large moisture spread after drying significantly devalues the product, especially in species with high green MC spread. Therefore, this research aims to optimize kiln-drying and provides a predictive approach to estimate and classify target timber moisture, using a gradient-boosting machine learning model. Inputs include three wood attributes (initial moisture, initial weight, and basic density) and three drying parameters (schedule, conditioning, and post-storage). Results show that initial weight has the highest correlation with the final moisture and possesses the highest relative importance in both predictive and classifier models. This model demonstrated a drop in training accuracy after removing schedule, conditioning, and post-storage from inputs, emphasizing that the drying parameters are significant in the robustness of the model. However, the regression-based model failed to satisfactorily predict the moisture after kiln-drying. In contrast, the classifying model is capable of classifying dried wood into acceptable, over-, and under-dried groups, which could apply to timber pre- and post-sorting. Overall, the gradient-boosting model successfully classified the moisture in kiln-dried western hemlock timber.

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