Punching Shear Retrofit of Reinforced Concrete Flat Slabs Subjected to Static and reversed Cyclic Loads
Bibliographic record
Abstract
The paper presents research program on retrofitting reinforced concrete slab-column connections to increase their punching shear strength and ductility in seismic regions. The current testing program includes five specimens, with and without shear reinforcement. The goal is to experimentally study the efficiency of shear bolt retrofitting technique in preventing collapse of flat concrete slabs in seismic regions. The proposed technique using shear bolt reinforcement allows repair and strengthening of existing, previously built flat reinforced concrete slabs supported on columns, which do not have adequate punching shear strength at the column area. A shear bolt consists of a headed vertical rod threaded at the other end for anchoring using a washer and nut system. The bolts are installed in holes drilled in a slab in concentric perimeters around the column. The presented results of the experimental work include large-scale interior reinforced concrete slab-column connections tested under vertical and reversed cycling horizontal loads. The hysteretic response behaviour is presented which shows how transverse reinforcements increase punching shear capacity, ductility and energy dissipation capability of slab-column connections. Discussion related to crack formation and propagation, deformations and strains in the reinforcements is included. The computed capacities of the specimens are compared to the experimental values.
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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) | 0.001 | 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) | 0.002 | 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".