Experimental Study on the Quasi-Static Cyclic and Dynamic Performance of Two-Story Platform Cross-Laminated Timber Mock-Ups
Bibliographic record
Abstract
Cross-laminated timber (CLT) is a mass timber product that has been used increasingly as a sustainable and cost-effective alternative to conventional construction materials such as concrete and steel. Understanding the seismic performance of CLT buildings is vital in the design process for engineers as the use of CLT has increased toward mid- and high-rise buildings. In this study, the experimental dynamic behavior of three full-scale two-story platform CLT shear wall mock-ups were investigated under quasi-static reversed cyclic and interval impact hammer modal tests. The mock-ups differed in the tension splice between the two stories, the level of dead load, and the installation of an acoustic insulation layer between stories. The impact hammer modal tests were conducted during different steps of the reversed cyclic tests to extract modal characteristics such as natural frequencies, damping ratios, and mode shapes in the defined drifts of frequent, medium, and rare seismic hazard levels. The mock-up’s experimental first period was in good agreement with the results predicted by the empirical equation in the National Building Code of Canada (NBCC). The stiffness degradation during the reversed cyclic tests was related to frequency reductions representing an index of damage. The presence of an acoustic layer increased the damping ratio and stiffness in the first three measured modes. However, further investigations are needed to see the extent of the effect of acoustic layers between the walls and floors on the lateral performance of timber buildings equipped with CLT shear walls and floors.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".