Experimental and numerical behavior of strengthened reinforced concrete slabs
Bibliographic record
Abstract
Abstract In terms of the importance of establishing mega structures in Egypt, there is an increasing demand to strengthen their slabs. Accordingly, this research was initiated with the objective of assessing different strengthening techniques (strengthening by concrete jacket or strips of carbon fiber reinforced polymer “CFRP”), where a multi-disciplinary approach was employed. Principally, literature within the domain of slab strengthening was amassed and scrutinized. An experimental work was conducted to examine 5 specimens of slabs under 4-point loadings. Crack patterns, midspan deflections, and steel strains were designated, where load–deflection curves and load-strain curves were produced. Moreover, the experimental investigation was replicated numerically by ABAQUS. Experimental and numerical results were contrasted, from which it was apparent that their results provided comparable trends. Confident with this contrasting process, a parametric study was achieved by ABAQUS, where 54 specimens with various parameters were investigated. Results showed that strengthening with RC jacketing and CFRP strips enhanced the load capacity and initial stiffness while decreasing the ductility for RC slabs. In addition, various jacketing bar and CFRP properties changes showed significant enhancement in the behavior of strengthened slabs. The numerical results showed enhancement in the load capacity of strengthened specimens due to the increase in the jacketing yield strength, bar diameter, and the jacketing concrete compressive strength. Increasing the number of CFRP strips enhanced the performance of the strengthened slab specimens by 14.2% ~ 25.1%.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".