Numerical modelling of CLT shear walls with hyperelastic hold-downs
Bibliographic record
Abstract
The provisions of CSA O86 (2019) for Cross-laminated Timber (CLT) shear walls recommend the design of non-dissipative hold-downs with sufficient deformability to facilitate wall rocking. A hyperelastic hold-down system was proposed to satisfy these criteria. In this study, a numerical model was developed to capture the behaviour of CLT shear walls with the hyperelastic hold-down system using data from previous component level and full-scale shear testing. Six hold-down configurations were calibrated using the software OpenSees. Different modelling approaches were attempted; ultimately, a back calibration approach using the shear wall test data produced acceptable results. Calibration parameters were derived for the OpenSees ‘Hysteretic material’ for each hold-down configuration utilizing data from six tests on shear walls with un-coupled panels, and validated with the results from twelve additional tests on shear walls with coupled panels. The average differences between test and model for corner uplift, force at peak lateral displacement and energy dissipation were found to be 7%, 12% and 11%, respectively. A set of equations were proposed to predict calibration parameters of untested configurations. Finally, a two-storey platform-type shear wall was designed and modelled applying the calibrated Hysteretic material for the hyperelastic hold-downs. The developed model can be used to predict the shear wall performance of un-tested configurations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".