EXPERIMENTAL AND NUMERICAL ANALYSIS OF HIGH-CAPACITY SHEAR WALLS WITH MULTIPLE ROWS OF NAILS
Bibliographic record
Abstract
With the increase of building height in light wood-frame construction and seismic design spectra in the 2015 edition of National Building Code of Canada, stronger shear wall systems have been facing higher demands, especially for mid-rise wood-frame buildings located in high seismic zones.In collaboration with FPInnovations, a new high-capacity shear wall system with two and three rows of nails was developed.A total of 30 shear walls had been tested under reversed cyclic loading.Results showed that the lateral resistance of shear walls with multiple rows of nails is roughly proportional to the number of rows compared to a standard shear wall with the same sheathing thickness, nail diameter and nail spacing.However, new failure modes, such as splitting of bottom plates, out-of-plane separation of end studs from bottom plates, rupture of sheathing panels, etc. have limited the post-peak deformation of the high-capacity shear wall and its ductility.A better understanding on the stress-strain development of wood material and connections is needed to develop design details to prevent these failure modes and increase the ductility and design resistance of wood shear walls with multiple rows of nails.A preliminary 3D numerical model of high-capacity shear walls with multiple rows of nails were developed using ABAQUS to simulate the lateral performance and failure modes of high-capacity shear walls.Testing data from previous research by the authors was used to verify the modeling techniques developed in this study.Results show that the detailed 3D shear wall model can reasonably simulate the lateral resistance of highcapacity shear walls and the failure modes that are not common in regular shear walls.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".