Bibliographic record
Abstract
The objective of this work was to investigate the influence of the slab thickness and the layout of shear stud reinforcement on the behaviour of interior slab-column connections. To achieve this, experiments on 12 interior slab-column connections were conducted having the slab thickness and the layout of shear reinforcement as their major variables. The tests were designed using the shear friction model for slabs developed by Dechka (2001). The test results were included in a database developed to investigate the strengths and weaknesses of the shear friction model and the current Canadian design code (CSA Standard A23.3-94, 1994). The experimental program revealed a dependency of the punching capacity of interior slab-column connections on the slab thickness. The experiments also helped to identify rational patterns for shear reinforcement placement and allow for recommendations on the extent of such shear reinforcement. In the course of the theoretical investigation, the shear friction model was further developed to better reflect the actual punching behaviour of slab-column connections. The development mainly focuses on the location and shape of the failure cone. Due to the unsatisfactory performance of the current Canadian design code in the comparison of its punching load predictions to experiments on over 300 slab-column assemblies, recommendations are made to improve this design code. The recommendations are made in a form that they can be adapted easily to the current Canadian code.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".