Influence of cut–out openings on the flexural behavior of one–way concrete slabs strengthened with NSM–FRP bars and EB–CFRP strips
Bibliographic record
Abstract
Strengthening concrete slabs with cut-out openings is critical to maintain structural integrity in many retrofitting and repair applications. This paper presents both experimental and numerical findings on the flexural performance of reinforced concrete (RC) one–way slabs with cut–out openings. The slabs were strengthened using either near–surface mounted fiber–reinforced polymer (NSM–FRP) bars or externally bonded carbon fiber–reinforced polymer (EB–CFRP) strips. Twelve one–way RC slabs were tested, the key experimental variables included the strengthening technique (NSM and EB), NSM reinforcement ratio (0.17 and 0.35 %), NSM bar material (stainless–steel, basalt FRP, carbon FRP, glass FRP bars), and the number of cut–out openings (one and two). Experimental findings revealed substantial improvements in the ultimate capacity, with enhancements of 71–190 % for NSM-strengthened slabs and 164–183 % for EB-strengthened slabs compared to unstrengthened slabs with cut-out openings. Notably, NSM-strengthened slabs achieved up to 18 % higher ductility than the control slab without cut-out openings, while EB-strengthened slabs exhibited a significant ductility reduction of 62–63 % due to debonding failures in the EB-CFRP strips. The improved flexural capacity in NSM-strengthened slabs was attributed to the better anchorage and confinement of bars within the concrete cover, which delayed debonding and allowed for more effective stress transfer . Basalt FRP–NSM bars demonstrated performance comparable to glass FRP–NSM bars, while the number of cut-out openings had a negligible effect on ultimate capacity. Finally, finite element models were developed using Abaqus software package. The developed models reasonably predicted the nonlinear structural performance of the strengthened 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 | 0.000 | 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 | 0.000 | 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".