%0 Research article
%T Neural networks for predicting fracture toughness of individual wood samples
%A Samarasinghe, Sandhya
%A Kulasiri, Don
%A Jamieson, Tristan
%D 2007
%J Silva Fennica
%V 41
%N 1
%R doi:10.14214/sf.309
%U https://silvafennica.fi/article/309
%X Strain energy release rate (G<sub>Ic</sub>) 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 G<sub>Ic</sub>,  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 G<sub>Ic</sub> and it was 215 kPa.m<sup>0.5</sup>. 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 R<sup>2</sup> 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.