Punching Shear Behavior of Flat Slab Strengthen with Y-Type Perfobond Shear
Bibliographic record
Abstract
This study demonstrates the behavior of a flat slab experimentally and numerically with two different types of shear reinforcements. Ten slabs were cast and tested in the experimental part. The samples were divided into two groups; the first consists of steel fiber, and the second has no steel fiber. Two control samples were considered with only flexural reinforcement, while the other eight samples had two types of shear reinforcement; the first was rebar to form a Y-shape, while the other was a steel plate formed as a Y-type perfobond. Shear reinforcement is provided by employing radial and parallel shear reinforcement. The tested samples in the experimental part have been simulated numerically using the Abaqus/CAE program. As a result of the laboratory examination, it was determined that the addition of steel fibers increased the ultimate load by (7.4% to 20.58%) for models with steel fibers compared to models without steel fibers. It was determined that the presence of steel plates in the models increased their capacity by 2.4% for ultimate load and by (6% to 50%) for deflection compared to ultimate load and deflection in models with steel bars. In Abaqus, the models with steel fibers were found to be 12.7%-26.6% stronger than those without them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.003 | 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".