Experimental Study on Punching Shear Behavior of Ultra-High-Performance Concrete (UHPC) Slabs
Bibliographic record
Abstract
This study assesses the punching shear characteristics of ultra-high-performance concrete (UHPC) slabs in two phases. The initial phase involved experimental tests to determine the critical thickness differentiating punching shear failure and flexural failure modes. Subsequently, the second phase further explored the punching shear behavior of UHPC slabs by analyzing various key parameters. The experimental findings indicated that as the thickness of the slabs increased, the punching shear capacity exhibited nearly linear enhancement, surpassing the improvement seen in bending capacity. Thus, a critical thickness of at least 100 mm was identified as the threshold distinguishing punching shear failure from flexural failure. Additionally, an increase in slab thickness significantly elevated the cracking load of the UHPC slabs. While a higher reinforcement ratio of 3.5% slightly increased the first cracking load, it greatly enhanced the ultimate capacity. The addition of steel fibers also contributed to improvements in both cracking and ultimate loads, albeit to a limited extent. The use of a granite powder substitute, comprising 10% of the mass of silica fume, had minimal impact on the punching shear capacity of the UHPC slabs. Finally, a comparison is drawn between the experimental results for punching shear capacity and those obtained from various theoretical models. This comparison highlights significant discrepancies in the results, stemming from the differing parameters employed in the proposed theoretical models. Among the prediction models, the JSCE model provided the most balanced and conservatively accurate estimation of punching shear capacity, effectively incorporating the effects of slab thickness, reinforcement ratio, and fiber content, thus highlighting its potential as a reliable reference for future design recommendations. This information will serve as a valuable reference for future research and practical applications related to UHPC bridge decks and slabs.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".