In-Plane Strength and Stiffness of Cross-Laminated Timber Shear Walls
Bibliographic record
Abstract
The research presented herein investigated the in-plane performance of cross-laminated timber (CLT) shear walls for platform-type buildings under lateral loading. Finite element models of CLT connections (i.e., brackets, hold-downs and self-tapping screws) were developed in OpenSees and calibrated against experimental tests to represent the connections’ hysteresis behaviour under cyclic tension and shear loading. The results were incorporated into models of CLT single and coupled shear walls. The results in terms of peak displacement, peak load and energy dissipation were in good agreement when compared to full-scale shear wall tests. Subsequently, a parametric study of 56 single and 40 coupled CLT shear walls was conducted with varying numbers and types of connectors (wall-to-floor and wall-to-wall) for evaluating their seismic performance. It was found that the strength, stiffness and energy dissipation of the single and coupled CLT shear walls increased with an increase in the number of connectors. Single shear walls with hold-downs and brackets performed better under seismic loading compared to walls with brackets only. Similarly, coupled shear walls with four hold-downs performed better compared to walls with two hold-downs. Finally, ductility of coupled shear walls was found to be 31% higher compared to that of single shear walls. The findings from this research are useful for engineers to efficiently design CLT shear walls in platform-type construction.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".