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In this study, the time to crack initiation (Tinit), duration of crack propagation (Tfrac), 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. Tinit was consistent for all volumes and the average Tinit was nonlinearly related to volume and ROL. For the smallest ROL, there was a distinct volume effect on Tinit and the effect was negligble at the largest ROL. However, the stress at crack initiation was not consistent. Contrasting these, Tfrac 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.

Exploration of fracture dynamics properties and predicting fracture toughness of individual wood beams using neural networks