ArticleMaterials (Basel, Switzerland)2021
Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Effects of UV radiation on natural and synthetic materials.Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2023Article
- The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach.Polymers · 2023Article
- Advanced Eco-Friendly Wood-Based Composites.Materials (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
Funding
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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 R
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