Virtual Testing of Strong Wood Light-Frame Shear Walls under Monotonic and Cyclic Loads: Model Development, Validation, and Parametric Studies
Bibliographic record
Abstract
There is a growing demand for strong wood light-frame shear wall systems in mid-rise buildings, particularly in high-seismic zones. The best performance of these walls is achieved when most of the energy is dissipated through shear deformation in the sheathing-to-framing connectors (nails), while the framing and anchorage systems remain within their elastic regime. This study presents a numerical methodology for virtual characterization and analysis of strong wood light-frame shear walls subjected to large monotonic and cyclical loads, utilizing a 3D finite element (FE) model in the ABAQUS software. The accuracy of the predictions for both the nail connectors and the wall assembly is validated by comparing them with experimental data from the literature. The results indicate that a discrete hold-down system can overstress the end studs, increasing the risk of wood crushing. Furthermore, it is shown that optimizing the elastic properties and thickness of oriented strand board (OSB) sheathing panels can significantly improve the performance of wood light-frame shear walls without increasing the number of the nails.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".