Timber shear walls with large openings: experimental and numerical prediction of the structural behaviour
Bibliographic record
Abstract
A numerical model based on the finite element method is presented for prediction of the cyclic response of wood frame structures. The model predicts the cyclic response of shear walls. Nonlinear phenomena are assumed to be concentrated in the connections that are modelled through elements linking the structural elements including the posts, beams, and sheathing panels. Identification of model parameters relies on tests on individual connections. Connection tests on different nail lengths were conducted under monotonic and cyclic lateral loads. Based on the results from past studies that indicate the pull-through failure is an important failure mode in common nail connections with lumber and oriented strand board (OSB), washers were considered as a means to reinforce the connection. The influence of reinforced nailing on the static and dynamic performance of full-size wood frame shear walls with large openings, sheathed with OSB panels, was evaluated experimentally. Combinations of parameters were studied, such as the number of hold-downs, the panel shapes, the nail distribution, and the bracing systems. Comparisons of the dissipated energy per cycle revealed a higher capacity for walls using nails with washer reinforcement than without. Results from numerical simulations of the monotonic and cyclic tests performed on the walls are presented.Key words: timber shear wall, connections, finite element, dissipated energy.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".