Développement de modèles éléments finis de types volumique, volume-coque et volume-poutre pour l’analyse du comportement des structures multicouches en bois assemblées par des goujons
Bibliographic record
Abstract
Multilayered timber structures, assembled using densified wood dowels, represent a sustainable and innovative solution for the construction sector. The development of predictive finite element models requires a solid representation of the geometry for modelling the complex mechanical behaviour of these structures. However, solid models are costly, especially in the context of variability studies and optimization. In this thesis, solid, solid-shell, and solid-beam approaches are developed to obtain accurate models that can be considered as the best compromise. The study of the mechanical behaviour of multilayered timber structures reveals that the layers adopt a shell-like behaviour, while the dowels behave like beams. Higher-order displacement fields through the thickness of the layers and through the cross-section of the dowels are identified. To meet these displacement fields while maintaining a solid representation, two methods have been developed. A first method exploits standard solid elements by applying shell theories through the thickness of the layers and beam theories through the sections of the dowels. A second method uses a 32-node hexahedral element and is inspired by the principle of solid-shell and solid-beam elements, with a single element through the thickness of the layers and a single element through the section of the dowels. The results demonstrate that the methods proposed in this thesis lead to effective modelling tools for multilayered timber structures assembled with densified wood dowels. These methods offer perspectives for future developments and applications to other types of structures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".