Numerical Investigation of Slab-Column Connections with Various Reinforcement Ratios
Bibliographic record
Abstract
This work considers three-dimensional non-linear finite element models (FEMs) to obtain insight into the contribution of flexural reinforcement ratio to the failure mode of slab-column connections. The correlation between punching and flexural-punching failure modes is examined. The models are calibrated to simulate the punching shear failure of reinforced concrete flat slabs under vertical monotonic loading and a constant gravity load in combination with monotonic unbalanced moment using a smeared concrete model, denoted as Concrete Damage Plasticity (CDP). In this regard, previously tested slab-column connections with various reinforcement ratios are selected from literature. The numerical outcomes are validated in terms of load-deflection (or moment-deflection) curves. The comparison between test and numerical results shows that the numerical analyses using the CDP model can accurately predict the rotation and punching capacity along with the failure mode of the slabs. As a parametric investigation, the FEM is utilised to characterise further the failure process of slabs with reinforcing ratios varied from 0.2% to 2%. The numerical results are compared with predictions from the design code ACI 318-19 and the Critical Shear Crack Theory.
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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 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".