Modulus of rupture prediction in Pinus sylvestris with multivariate models constructed with resonance, ultrasound, and wood heterogeneity variables

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.