Bibliographic record
Abstract
Brittle punching failure of flat plates can occur as a result of transfer of shearing forces and unbalanced moments between slabs and columns. During an earthquake, the unbalanced moments transferred between slabs and columns can produce significant shear stresses that increase the likelihood of brittle failure. This brittle punching failure mode must be avoided for seismic-resistant flat plate structures. The most common punching strengthening methods are provision of the slab-column connections with drop panels or shear reinforcement or use of high strength concrete in the slab at the vicinity of the connections. This paper compares the effect of these punching strengthening methods on the ductility of slab-column connections. The results of a part of an extensive experimental program conducted on edge slab-column connections, without and with shear reinforcement, are presented. The experiments show that provision of stud shear reinforcement results in seismic-resistant slab-column connections, in which brittle punching failure is avoided in severe earthquakes. The connections with stud shear reinforcement can undergo ductile deformations associated with up to 5% lateral interstorey drift ratio, without loss of resistance to punching due to gravity loads. Key words: concrete design, ductility, energy dissipation, flat slabs, lateral drift, moment transfer, punching, seismic, shear strength, slab-column connections, stiffness, stud shear reinforcement.
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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) | 0.000 | 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) | 0.003 | 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".