Behavior and punching capacity of flat slabs with the rational use of UHPFRC: NLFEA and analytical predictions
Bibliographic record
Abstract
The outstanding mechanical properties of ultra-high-performance fiber-reinforced concrete (UHPFRC) can be used to improve the punching behavior of new slab-column connections. This study investigates the punching capacity of flat slab-column connections built with a rational combination of normal strength concrete (NSC) and UHPFRC in critical shear regions through non-linear finite element analyses (NLFEA) and by a punching shear model based on the critical shear crack theory (CSCT). Ten control tests from the literature were used to validate the Finite Element Models (FEM) developed to capture the behavior of slab-column connections made entirely with NSC, UHPFRC, and a combination of both materials. Parametric analyses were performed to investigate the behavior of connections with the rational use of UHPFRC, varying the reinforcement ratio, area, and thickness of the UHPFRC layer. The results indicated that placing a UHPFRC layer near the column in the slab compression zone significantly increases the punching capacity and deformation capacity compared to placing UHPFRC on the tensile side. The punching capacity enhancements varied between 26% and 156%, according to the reinforcement ratios and configurations of the UHPFRC layer investigated. The mean ratio between predicted punching capacities by advanced NLFEA and the analytical method proposed was 1.09, with a coefficient of variation of 10.3%. Therefore, the results indicated that the design of flat slabs with UHPFRC at the critical shear regions is a viable solution. Finally, the CSCT model can be used in design to predict the punching capacity of these connections.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".