EXPERIMENTAL TESTING OF MIXED ANGLE SCREWED HOLD-DOWN CONNECTIONS FOR CLT SHEAR WALLS
Bibliographic record
Abstract
Cross laminated timber (CLT) shear walls are an efficient lateral load resisting system for mass timber buildings.Ductility and energy dissipation of timber buildings is mostly provided by well detailed connections.Therefore, to achieve good seismic performance of CLT shear walls, hold-down connections must be not only strong and stiff but also ductile with sufficient displacement capacity to meet the drift demands.A novel hold-down connection is proposed using a mixture of screws installed at an inclined angle and 90° to the grain.The respective benefits of inclined and 90° screws can be combined to create a strong, stiff, and ductile connection.Two stages of experimental testing were undertaken with target connection capacities of 600 kN and 1200 kN respectively.The experimental results confirmed that mixed angle screws can provide a strong, stiff, and ductile hold-down solution for CLT shear walls.The optimal ratio of inclined screws to 90° screws was 1:2, and the optimal ratio of 12 mm inclined screws to 12 mm 90° screws was 1:1.5.The primary failure modes were screw withdrawal and wood embedment crushing.Only localised damage of timber around the screw holes was observed and this was repaired with epoxy.New screws were then reinstated with a small offset to the original screw locations and the repaired hold-down connections were found to have the same or even slightly better performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".