Hong Ling, Sandhya Samarasinghe, G. Don Kulasiri. (2009). Modelling variability in full-field displacement profiles and Poisson ratio of wood in compression using stochastic neural networks. Silva Fennica vol. 43 no. 5 article id 460. https://doi.org/10.14214/sf.460

Keywords: wood; digital image correlation; displacement profiles; variability; micro structure; stochastic neural networks; Poisson ratio

Abstract | View details | Full text in PDF | Author Info

Vertical and horizontal displacement profiles in compression parallel-to-grain in a 20 x 20 mm area (30 x 21 or 630 points) in the Tangential–Longitudinal (T–L) and Radial Longitudinal (R–L) sections of small wood columns were obtained from digital image correlation applied to simultaneously captured images of the two surfaces. These consisted of 21 displacement realisations of 30 points along the length of the specimen. They revealed considerable local variations. Stochastic neural networks were successfully developed to simulate trends and noise across and along a specimen in both displacements as well as Poisson ratios in T–L and R–L sections for two selected load levels of 20kN and 40kN. These networks specifically embed noise characteristics extracted from data to generate realistic displacement and Poisson ratio realisations with inherent variability. Models were successfully validated using independent data extracted based on bootstrapping method with high accuracy with R² ranging from 0.79 to 0.91. The models were further validated successfully using a second approach involving Confidence Intervals generated from the data extracted from the models. Models and experimental results revealed that for 20kN load, both vertical and horizontal displacements in T–L section were less heterogeneous across the specimen (smaller vertical shearing and horizontal strain, respectively) than those in the R–L section. For the 40kN load, both displacement profiles in the T–L section were less noisy and more compact than those for the 20kN load indicating less heterogeneity due to compaction of structure. In the R–L section, larger vertical shearing and horizontal strains persisted at 40 kN load. Poisson ratio decreased with load and it was nonlinear in both sections but T–L section showed much less noise across the specimen than the R–L section.

Ling, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Canterbury, New Zealand E-mail: hl@nn.nz
Samarasinghe, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Canterbury, New Zealand E-mail: sandhya.samarasinghe@lincoln.ac.nz
Kulasiri, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Canterbury, New Zealand E-mail: gdk@nn.nz

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

Keywords: Pinus radiata; wood properties; cracks; initiation; New Zealand; peak stress; speed; video imaging

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In this study, the time to crack initiation (T_init), duration of crack propagation (T_frac), 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_init was consistent for all volumes and the average T_init was nonlinearly related to volume and ROL. For the smallest ROL, there was a distinct volume effect on T_init and the effect was negligble at the largest ROL. However, the stress at crack initiation was not consistent. Contrasting these, T_frac 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 E-mail: sandhya.samarasinghe@lincoln.ac.nz

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

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

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Strain energy release rate (G_Ic) 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_Ic, 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_Ic and it was 215 kPa.m^0.5. 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 E-mail: ss@nn.nz
Kulasiri, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand E-mail: dk@nn.nz
Jamieson, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand E-mail: tj@nn.nz

article id 415, category Research article

Sandhya Samarasinghe, Don Kulasiri. (2004). Stress intensity factor of wood from crack-tip displacement fields obtained from digital image processing. Silva Fennica vol. 38 no. 3 article id 415. https://doi.org/10.14214/sf.415

Keywords: Pinus radiata; wood; fracture toughness; stress intensity factor; digital image correlation; orthotropic fracture theory

Abstract | View details | Full text in PDF | Author Info

Stress intensity factor of radiata pine (Pinus radiata) in Tangential-Longitudinal opening mode was determined from crack-tip displacement fields obtained from digital image correlation in conjunction with orthotropic fracture theory. For lower loads, experiments agreed with the linear elastic fracture theory but for higher loads, stress intensity factor and load relationship was nonlinear. For 41% of the specimens tested, tip-displacement based stress intensity factor agreed with that based on the ASTM standard formula for lower loads but deviated for higher loads closer to failure. The tip displacement plots showed that the nonlinear behaviour is due to large displacements which we attributed to large plastic deformations and/or micro-cracking in this region. The other 59% specimens showed a similar trend except that the crack-tip based stress intensity factor was consistently higher than the value obtained from the standard formula. The fracture toughness from tip displacements was larger than the standard values for all specimens and the two were related by a logarithmic function with an R2 of 0.61. The study also established that fracture toughness increases with the angle of inclination of the original crack plane to the Radial Longitudinal plane.

Samarasinghe, Lincoln University, P.O. Box 84, Canterbury, New Zealand E-mail: ss@nn.nz
Kulasiri, Lincoln University, P.O. Box 84, Canterbury, New Zealand E-mail: kulasird@lincoln.ac.nz

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