Bibliographic record
Abstract
A nonlinear stochastic model has been formulated to simulate the stress-strain behavior of strand-based wood composites based on the constitutive properties of the wood strands. Prediction models of this type save time and money in the development of wood composites by computationally gauging the effects of varying raw material characteristics with limited fabrication and testing of the full-scale product. The proposed model uses a stochastic-based materially nonlinear finite-element code with extended capacity to perform Monte Carlo simulations to predict the stress-strain behavior of [±15]s and [±30]s angle-ply laminates in tension and compression. The nonlinear constitutive behavior of the wood strands is characterized within the framework of rate-independent theory of orthotropic plasticity, where the plastic flow rule is in accordance with the Tsai-Wu criterion. Shear strength and stiffness of the strands, as well as the interaction parameter of the Tsai-Wu criterion have been estimated through a minimization technique developed in the present study. The model's accuracy was validated through comparisons of the numerical simulation results and experimental data. Excellent agreement was found.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".