Finite element analysis of punching shear behavior of reinforced concrete slabs supported on walls
Bibliographic record
Abstract
Abstract Due to the low lateral stiffness of slabs supported on columns alone, reinforced concrete flat plates are typically combined with other structural elements, such as shear walls. In these structures, the slab–column connections are designed to carry gravity loads only, and the shear walls are required to resist both gravity and lateral forces. Therefore, the slab–wall connections are essential for the performance of both the gravity and lateral force resisting systems. However, most punching shear research and design provisions are focused on slab–column connections, even though punching failures around slab–wall connections have occurred experimentally. Empirical testing of slab–wall connections is difficult due to the specimen size. This paper investigates the punching shear behavior of interior slab–wall connections subjected to concentric vertical loading, and combined concentric vertical loading and uniaxial unbalanced moment using a plasticity‐based nonlinear finite element model (FEM) in ABAQUS. The analysis of isolated slab–wall connections demonstrates that punching failures can occur before one‐way shear failures. Since the overall connection capacity is much higher than the expected loads in most structures, if these punching failures happen they would be localized to the region around the wall end and are not expected to lead to structural collapse.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".