%0 Research article
%T Exploration of fracture dynamics properties and predicting fracture toughness of individual wood beams using neural networks
%A Samarasinghe, Sandhya
%D 2009
%J Silva Fennica
%V 43
%N 2
%R doi:10.14214/sf.212
%U https://silvafennica.fi/article/212
%X In this study, the time to crack initiation (T<sub>init</sub>), duration of crack propagation (T<sub>frac</sub>),  crack initiation stress, peak stress as well as crack speed and  fracture toughness were investigated for three Rates of Loading (ROL)  and four sizes of notched wood beams using high-speed video imaging and  neural networks. T<sub>init</sub> was consistent for all volumes and the average T<sub>init</sub> was nonlinearly related to volume and ROL. For the smallest ROL, there was a distinct volume effect on T<sub>init</sub> and the effect was negligble at the largest ROL. However, the stress at  crack initiation was not consistent. Contrasting these, T<sub>frac</sub> for all volumes appeared to be highly variable but the peak stress  carried prior to catastrophic failure was consistent. The crack  propagation was a wave phenomenon with positive and negative (crack  closure) speeds that varied with the ROL. As accurate estimation of  crack initiation load (or stress) and its relationship to peak load (or  stress) is important for determining fracture toughness, Artificial  Neural Networks (ANN) models were developed for predicting them from  volume, Young’s modulus, face and grain angles, density, moisture  content and ROL. Models for crack initiation load and peak load showed  much higher predictive power than those for the stresses with  correlation coefficients of 0.85 and 0.97, respectively, between the  actual and predicted loads. Neural networks were also developed for  predicting fracture toughness of individual wood specimens and the best  model produced a statistically significant correlation of 0.813 between  the predicted and actual fracture toughness on a validation dataset. The  inputs captured 62% of variability of fracture toughness. Volume and  Young’s modulus were the top two contributing variables with others  providing lesser contributions.