Longitudinal, transverse and ultrasound vibration for the prediction of stiffness using models incorporating features in Pinus sylvestris timber

Non-destructive testing was used to predict the static modulus of elasticity (MOE_S) of Scots pine (Pinus sylvestris) timber from the northeast of Spain. Three vibration tests were performed, longitudinal, flatwise and edgewise, to obtain the dynamic modulus of elasticity (MOE_dyn) based on the fundamental resonant frequencies. The MOE_dyn was additionally obtained from ultrasound tests. Measurements of different features were performed of the various samples, which were also subjected to a bending test to find the MOE_S. Different types of models, simple linear regression (SLR), multiple linear regression (MLR) and artificial neural network (ANN), were generated to predict the MOE_S based on the study variables. The predictive capacity of the different models was analysed by comparing the root mean square error (RMSE) obtained using the tenfold cross-validation method. The vibration techniques showed a better MOE_S prediction than the ultrasound techniques. The MOE_dyn obtained from the fundamental resonant frequency of the edgewise flexural vibration (MOE_EV) was the variable that best predicted the MOE_S. The error of the SLR with MOE_EV was not significantly improved by any other model, whether univariate or multivariate. The ANN-based models did not significantly improve the error of the MLR-based models.