Thick-shell finite element analysis of reinforced concrete flat plates with non-uniform connection stresses
Bibliographic record
Abstract
This paper presents the application of a thick-shell nonlinear finite element analysis procedure to estimate the punching shear resisting performance of reinforced concrete slab-column connections under variable connection shear stress conditions. In the analyses performed, varied connection shear stress conditions stem from slabs being supported by columns with different cross-section aspect ratios, being subjected to different distributions of gravity loading, being constructed with different planar reinforcement ratios in orthogonal directions, as well as the application of unbalanced bending moments. Forty-eight isolated slab-column connection specimens presented in the literature were modelled and analyzed using a thick-shell finite analysis procedure. All numerical results were developed using a predefined set of material models and analysis parameters, a consistent meshing procedure, and are shown to provide meaningful agreement with experimental data without the need for case-specific model calibration or the adoption of specialised failure criteria. The results show that thick-shell modelling methods can be suitable for estimating the punching shear response of slabs subjected to non-uniform shear stress development in connection regions, and can provide similar levels of precision to that obtained for the analysis of idealised slab-column connections typically used for design procedure and model development.
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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) | 0.001 | 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 | 0.001 | 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".