Finite Element Modeling of GFRP-Reinforced Concrete Interior Slab-Column Connections Subjected to Moment Transfer
Bibliographic record
Abstract
A finite element model (FEM) was constructed using specialized three-dimensional (3D) software to investigate the punching shear behavior of interior slab-column connections subjected to a moment-to-shear ratio of 0.15 m. The FEM was then verified against the experimental results of full-scale interior slab-column connections reinforced with glass fiber reinforcement polymer (GFRP) bars previously tested by the authors. The FEM results showed that the constructed model was able to predict the behavior of the slabs with reasonable accuracy. Afterward, the verified model was used to conduct a parametric study to investigate the effects of reinforcement ratio, perimeter-to-depth ratio, and column aspect ratio on the punching shear behavior of such connections. The test results showed that increasing the tested parameters enhanced the overall behavior of the connections in terms of decreasing deflections and reinforcement strain and increasing the ultimate capacity. In addition, the obtained punching shear stresses of the connections were compared to the predictions of the Canadian standard and the American guideline for FRP-reinforced concrete structures.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".