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Silva Fennica 1926-1997
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Articles containing the keyword 'video imaging'.

Category: Research article

article id 212, category Research article
Sandhya Samarasinghe. (2009). Exploration of fracture dynamics properties and predicting fracture toughness of individual wood beams using neural networks. Silva Fennica vol. 43 no. 2 article id 212. https://doi.org/10.14214/sf.212
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.
  • Samarasinghe, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Canterbury, New Zealand ORCID ID:E-mail: sandhya.samarasinghe@lincoln.ac.nz (email)
article id 309, category Research article
Sandhya Samarasinghe, Don Kulasiri, Tristan Jamieson. (2007). Neural networks for predicting fracture toughness of individual wood samples. Silva Fennica vol. 41 no. 1 article id 309. https://doi.org/10.14214/sf.309
Strain energy release rate (GIc) of Pinus radiata in the TL opening mode was determined using the compliance crack length relationship. A total of 123 specimens consisting of four sizes of specimen with each size having four different crack lengths were tested. For each specimen, grain and ring angles, density and moisture content were measured. Video imaging, was used to measure crack length during propagation. Since cracks extended in stages, full compliance-crack length relationship was developed for each specimen based on their initial and subsequent crack lengths. No significant differences in GIc, between initial and subsequent crack lengths were found for the smaller specimens by paired sample t-tests, but differences were significant for the largest specimen size. The Average fracture toughness was calculated from GIc and it was 215 kPa.m0.5. Three artificial neural networks were developed to predict the: 1) force required to propagate a crack, 2) crack extension, and 3) fracture toughness of an individual specimen. Each was successful, producing respective R2 of 0.870, 0.865, and 0.621 on validation data. A sensitivity analysis of the networks revealed that the crack length was the most influential with 21% contribution followed by grain angle with 14% contribution for predicting the applied force. This was followed by volume and physical properties. For predicting the crack extension, density had the greatest contribution (20%) followed by previous crack length and force contributing 16% equally. Fracture toughness was dominated by the dimensional parameters of the specimen contributing (42%) followed by anisotropy and physical properties.
  • Samarasinghe, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand ORCID ID:E-mail:
  • Kulasiri, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand ORCID ID:E-mail:
  • Jamieson, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand ORCID ID:E-mail:

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