Abstract
Multivariate models with multiple linear regression (MLR), artificial neural network (ANN), and k-nearest neighbors (KNN) were developed to predict the modulus of rupture of Pinus sylvestris structural timber. The aim of this study was to develop and compare these models obtained from resonance and ultrasound tests, static modulus of elasticity tests, and different measured wood feature. Resonance tests were performed in the three vibration modes (edgewise, flatwise, and longitudinal) to obtain the fundamental resonant frequencies. To compare the goodness-of-fit of the different models, the 10-fold cross-validation method was used, which proved to be an adequate strategy to avoid overfitting. The variable with the best predictive capacity of the modulus of rupture was knottiness. The error was notably lower in the multivariate than the univariate models. The ANN and KNN algorithms showed no improvement over the MLR. The most suitable MLR for prediction of the modulus of rupture was the model with four variables: knottiness, edgewise dynamic modulus of elasticity, velocity of ultrasounds, and longitudinal resonant frequency.
Highlights
Resonant frequencies have been employed to determine bending strength of Pinus sylvestris wood
Some authors have analysed the effect on the modulus of rupture (MOR) of features of sawn timber such as knots (Arriaga et al 2014; França et al 2019; Villasante et al 2019), the slope of grain (Arriaga et al 2014), or the rate of growth (Martins et al 2017; França et al 2019)
Some studies have taken into account the combined effect of different variables, while using multiple linear regression (MLR) for MOR prediction, with different pine species (Arriaga et al 2012; Martins et al 2017; Villasante et al 2019)
Summary
Resonant frequencies have been employed to determine bending strength of Pinus sylvestris wood. Most of the works (Arriaga et al 2012; Hassan et al 2013; Aira et al.2019) only used one vibration mode (edgewise, flatwise, or longitudinal) to predict the modulus of rupture (MOR). Few studies have used a combination of vibration and ultrasound techniques (Halabe et al 1997; Hassan et al.2013). Some studies have taken into account the combined effect of different variables, while using multiple linear regression (MLR) for MOR prediction, with different pine species (Arriaga et al 2012; Martins et al 2017; Villasante et al 2019). The coefficient of determination (R2) is used to compare the model’s capacity to predict MOR
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