Failure analysis of columnslab connections with stud shear reinforcement
Bibliographic record
Abstract
The design of a flat plate structure is generally governed either by serviceability limits on deflection or punching shear strength of the columnslab connections. To increase the strength of a columnslab connection, a new type of shear reinforcement, referred to as shear stud, is gaining popularity in practice. In this paper, a nonlinear layered finite element method (LFEM) is used to investigate the effectiveness of the shear studs in increasing the punching shear strength of edge and corner columnslab connections. In total, nine large-scale reinforced concrete slabs of a flat plate floor in the vicinity of edge and corner columns, tested previously in the laboratory, are analysed. All the slabs contained stud shear reinforcement (SSR) except a control slab where no SSR was provided. The test variables were the column size and the ratio of stud spacing to slab thickness. The punching shear strengths, loaddeflection responses, and crack patterns predicted by the LFEM are compared with the experimental results. The numerical investigation confirms the accuracy and effectiveness of the LFEM in predicting the strength of columnslab connections with SSR.Key words: columnslab connection, concrete flat plate, punching shear, stud shear reinforcement, finite element analysis.
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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 | 0.001 | 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) | 0.001 | 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".