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Sandhya Samarasinghe (email), Don Kulasiri, Tristan Jamieson

Neural networks for predicting fracture toughness of individual wood samples

Samarasinghe S., Kulasiri D., Jamieson T. (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

Abstract

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.

Keywords
strain energy release rate; video imaging; Neural Networks; fracture toughness; New Zealand; Pinus radiata

Author Info
  • Samarasinghe, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand ORCID ID:(email)
  • Kulasiri, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand ORCID ID:
  • Jamieson, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand ORCID ID:

Received 17 July 2006 Accepted 30 January 2007 Published 31 December 2007

Available at https://doi.org/10.14214/sf.309 | Download PDF

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