Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning

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-2 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.

Automated summary

Color parameters were used to develop a machine learning model for predicting the mechanical properties of artificially weathered wood samples. The study found a correlation between color change and mechanical degradation in the samples. The predictive model estimated the MOE and MOR values with high accuracy, suggesting potential for studying larger timber samples in future research.