Numerical assessment of random distribution of strands and voids oneffective mechanical properties of strand-based wood composites
Bibliographic record
Abstract
Strand-based wood composites are increasingly being used in the construction of low- to mid-rise timber buildings in Canada. In this study, the effects of voids and distribution of strands on the effective mechanical properties of a typical strand-based wood product, parallel strand lumber (PSL), are examined. A powerful numerical algorithm using python scripting has been developed for this purpose. A script is employed to distribute voids and strands through a large representative volume element (RVE) of a PSL beam. The effective properties of 1543 RVE realizations with different distributions of strands and voids are calculated and results are presented for each engineering constant. Unlike previous studies, strands and voids are distributed explicitly through the material’s cross-section. Hence, the role of small variations of strand and resin distribution, as well as presence of voids and their distribution among strands in the effective properties of PSL can be elucidated more realistically. Once strands and voids are distributed, a computational homogenization scheme is employed by applying periodic boundary conditions to each constructed RVE. Within the RVE, strands and voids are distributed based on a pre-defined distribution function with angles ranging from -10o to +10o, representing the variation of strands’ angles in the manufacturing process of PSL. Using the developed computational algorithm, the effect of RVE’s dimension on predicted properties was also studied. The effect of void’s volume fraction on the Young’s modulus along the strands’ longitudinal direction ( E11 ) is found to be much lower than the moduli in other directions (i.e. E22 and E33 ). The advantages of the proposed approach compared to similar approaches are discussed at the end.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".