Experimental and Numerical Investigations of Punching Shear Strengthening of Slab-Circular Column Connection Incorporating UHPC and Galvanized Threaded Steel Bolts
Bibliographic record
Abstract
Punching shear failure poses a critical risk in flat slab–column structures, potentially leading to catastrophic collapses. Retrofitting methods typically involve flexural or shear strengthening. Recent studies, however, reveal that combining indirect flexural strengthening with direct shear strengthening augments the punching shear performance. This research employed ultra-high-performance engineered cementitious composites (UHP-ECC) and ultra-high-performance steel-fiber-reinforced concrete (UHP-SFRC) as a bonded layer on the slab’s compression zone confining column as indirect flexural strengthening and galvanized threaded steel bolts as direct shear strengthening through slab thickness to augment the punching shear capacity. Six square flat slabs with central circular columns were constructed and then experimented to collapse to verify the effect of this proposed strengthening technique. The effects of various mesh and concrete types are investigated. Results showed that combining the UHP-ECC or UHP-SFRC bonded layer in the compression side with bonded galvanized threaded steel bolts significantly enhanced the punching shear strength of the slabs. The experimental findings demonstrated a remarkable increase of 62% and 111% over the unstrengthened slab for the UHP-ECC and UHP-SFRC strengthened slabs with single-layer mesh, respectively. Further enhancements were observed by adding a second steel reinforcement mesh to the UHP-bonded layer. A numerical model was developed using the finite-element (FEM) method to predict the structural behavior of tested slabs. Numerical results revealed that the FEM predicts well the performance of the slab–column connection, aligning well with experimental findings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".