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

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.

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

“…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).…”

Acoustic emission monitoring of wood materials and timber structures: A critical review

“…Different NDE methods including color measurement, X-ray computed tomography (X-ray CT), thermography, near-infrared spectroscopy (NIRS), and wave propagation methods were used, as reported in the literature, to assess the physical and mechanical properties of wood. The color measurement method uses the variation in wood color caused by different processes such as the thermal treatment or aging to predict the physical and mechanical properties of wood, such as the moisture content (MC), density, porosity, water absorption, swelling coefficient, and dynamic modulus of elasticity (MOE) [12][13]. The thermal treatment of wood makes the pale wood color darker [14].…”

Wood moisture monitoring and classification in kiln‐dried timber

“…A decision tree is also a tool for data analysis that enables interpretation of results, which is a considerable challenge of most ANNs (black box issue). It has been successfully used for internal checks detection in naturally weathered timber 43 and for predicting the mechanical properties of ultraviolet‐weathered wood 44,45 …”