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

Vahid Nasir; Hamidreza Fathi; Arezoo Fallah; Siavash Kazemirad; Farrokh Sassani; Petar Antov

doi:10.3390/ma14216314

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

Materials (Basel). 2021 Oct 22;14(21):6314. doi: 10.3390/ma14216314.

Authors

Vahid Nasir¹, Hamidreza Fathi², Arezoo Fallah², Siavash Kazemirad², Farrokh Sassani¹, Petar Antov³

Affiliations

¹ Department of Mechanical Engineering, The University of British Columbia (UBC), Vancouver, BC 2054-6250, Canada.
² School of Mechanical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
³ Department of Mechanical Wood Technology, Faculty of Forest Industry, University of Forestry, 1797 Sofia, Bulgaria.

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.

Keywords: artificial weathering; color change; machine learning; mechanical properties; photodegradation; ultraviolet radiation; wood characterization.