Abstract
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 R2 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.
Highlights
A machine learning model was developed based on the color parameters for predicting the modulus of elasticity (MOE) and modulus of rupture (MOR) values of weathered wood
Wood species that experienced greater color changes exhibited a more highlighted mechanical degradation. This indicates that the color parameters may be linked to the MOE and MOR of weathered wood
The mechanical properties of the samples could be predicted by the color parameters, the developed machine learning model needed the type of wood species as an input parameter for accurate prediction
Summary
Nondestructive evaluation (NDE) of wood is crucial for monitoring purposes and timber grading [1,2], especially when the wood is used in load-carrying applications. The characterization of the mechanical properties of wood, including the modulus of elasticity (MOE) and modulus of rupture (MOR), can be performed using tensile, compression, and bending static tests [3]. These methods are costly and time-consuming, and are not suitable for in situ characterization and monitoring purposes. While visual strength grading is being practiced in some industrial applications [4], fast and reliable assessment of timber and wood-based materials that accounts for material variability and anisotropic properties and natural defects requires the application of NDE methods
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