%0 Research article
%T Modelling variability in full-field displacement profiles and Poisson ratio of wood in compression using stochastic neural networks
%A Ling, Hong
%A Samarasinghe, Sandhya
%A Kulasiri, G. Don
%D 2009
%J Silva Fennica
%V 43
%N 5
%R doi:10.14214/sf.460
%U https://silvafennica.fi/article/460
%X 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 R<sup>2</sup> 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.