High-capacity hyperelastic hold-down for cross-laminated timber shear walls
Bibliographic record
Abstract
Cross-laminated timber (CLT) continues to establish a stronger footing in the Canadian construction industry, also as an option for lateral load resisting systems, such as shear walls. Recent modifications to the Canadian Standard for Engineering Design in Wood (CSA O86- 19) allow only rocking kinematics as energy dissipative mechanics for CLT shear walls, whereby hold-down must remain elastic. These provisions necessitate the development of novel hold-down solutions. In this report, the performance of a hyper-elastic high-capacity hold-down was investigated at the component level through tests on: (1) hold-down steel rod, (2) CLT housing, and (3) hold-down assemblies with different sizes of rubber pads. The tests demonstrated that: i) the rubber hold-down can remain elastic under a rocking kinematics provided that the elastic limit of the steel rod is not exceeded; ii) failure of the rod is the subsequent desired ductile mode; iii) the CLT width influences the failure mode; iv) the shape factor influences the achievable deformation of the rubber pad; v) increasing the rubber pad thickness reduces the hold-down stiffness; and vi) increasing the rubber pad width increases the hold-down stiffness. Numerical modelling and optimization suggested that using an intermediate steel laminate between layers of rubber pads could improve its performance. Based on the results of the investigations presented herein, a capacity-design procedure for the hyper-elastic hold-downs was proposed.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".