In-Plane Stiffness of Hybrid Steel–Cross-Laminated Timber Floor Diaphragms
Bibliographic record
Abstract
The use of hybrid structural systems formed by combining wood with other materials provides engineers with a wide range of solutions to satisfy code requirements for mid- and possibly high-rise buildings. Hybrid wood-based systems include a wide range of construction methods and different levels of integration of materials and components. The research discussed in this paper presents an innovative hybrid timber-steel solution for floor diaphragms developed by joining cross-laminated timber panels with cold-formed customized steel beams. The repeatable floor unit is prefabricated off site and then fastened on site using preloaded bolts and self-tapping screws. The paper discusses the in-plane stiffness of the floor and the distribution of the horizontal loads to the shear walls. A numerical model was developed with its input parameters calibrated from full-scale experimental tests. The effects of connections and element arrangement, load and constraint conditions, the relative stiffness of shear walls and their spacing, as well as the floor aspect ratio on the load-displacement response of floors has been investigated. The findings allow engineers to find the fundamental design parameters and evaluate their role in the diaphragmatic behavior of floors. In particular, unfavorable layouts for the shear walls must be avoided in order to prevent an excessive in-plane deformation of the buildings.
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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 | 0.000 |
| 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 | 0.000 | 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".