Predicting the strength of European beech (Fagus sylvatica L.) boards using image-based local fibre direction data
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2022-01
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Journal Article
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Abstract
Image-based local fibre direction data, generated based on the analysis of the medullary spindle pattern, were used to improve the prediction of the tensile strength parallel to the grain of European beech (Fagus sylvatica L.) boards. An approach to characterise the local fibre orientations in a board using a single numerical grading parameter was further developed. This parameter was used, in combination with the dynamic modulus of elasticity, to develop a regression model providing very good predictions of the experimentally determined tensile strength parallel to the grain (R2 = 0.84). Subsequently, machine-learning techniques were used to improve the strength model. Non-destructive and destructive tests were performed on (N =) 47 boards. A data (sub-) set (n = 36) was used to train different machine-learning techniques (Support-Vector Machines, Decision Tree, Random Forest, and Artificial Neural Network) using a 6-k cross-validation approach. The generalisation ability of the models was then assessed by a hold-out dataset (n = 11). The results showed that all machine-learning models presented good prediction accuracy (R2 up to 0.88 and MAPE below 8%). The support-vector machine and random forest methods showed the best performance. The combination of experimental methods with machine learning allows for a more precise strength grading of timber and, thus, can contribute to a more resource-efficient use of wood and may open new and more demanding fields for high-level timber applications in structures.
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56 (1)
Pages / Article No.
123 - 146
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Springer
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08809 - Frangi, Andrea (Tit.-Prof.)