Punching strength of continuous concrete slabs
Bibliographic record
Abstract
Punching tests on continuous concrete slabs are very expensive and elaborate. As a consequence, the majority of the existing punching tests have been performed on isolated specimens representing only the hogging moment region around the considered slab–column connection. Isolated punching tests are valuable in order to analyse the punching shear behaviour systematically. Nevertheless, beneficial effects of slabs' continuity (e.g. moment redistributions and membrane actions) may not occur in these tests. To investigate the punching shear behaviour of continuous concrete slabs, in this paper the existing two-parameter kinematic theory for punching shear in reinforced and prestressed concrete slabs is applied. The beneficial effects of slab continuity on punching strength are estimated based on a theoretical model described in the literature. The kinematic theory is applied to evaluate punching tests on a continuous slab, revealing good agreement between predictions and experimental results. As only few punching tests on continuous slabs are available, further parametric studies are performed to analyse the effects of slab continuity on punching strength in more detail. The results suggest higher punching shear capacities for continuous slabs than for isolated slabs. Nevertheless, the beneficial influences of slab continuity on punching strength strongly depend on the slab stiffness and flexural cracking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".