Progressive Damage Simulation of Wood Veneer Laminates and Their Uncertainty Using Finite Element Analysis Informed by Genetic Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Within the search for alternative sustainable materials for future transport applications, wood veneer laminates are promising, cost-effective candidates. Finite element simulations of progressive damage are needed to ensure the safe and reliable use of wood veneers while exploring their full potential. In this study, highly efficient finite element models simulate the mechanical response of quasi-isotropic [90/45/0/−45]s beech veneer laminates subjected to compact tension and a range of open-hole tension tests. Genetic algorithms (GA) were coupled with these simulations to calibrate the optimal input parameters and to account for the inherent uncertainties in the mechanical properties of wooden materials. The results show that the continuum damage mechanistic simulations can efficiently estimate progressive damage both qualitatively and quantitatively with errors of less than 4%. Variability can be assessedthrough the relatively limited number of 400 finite element simulations as compared to more data-intensive algorithms utilised for uncertainty quantification.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it