Experimental and numerical study on the effect of repairing reinforced concrete cracked beams strengthened with carbon fibre reinforced polymer laminates
Bibliographic record
Abstract
This paper presents experimental and numerical investigations on the effect of repairing cracks in reinforced concrete (RC) beams prior to strengthening them with carbon fibre reinforced polymer (CFRP) laminates. The experimental campaign comprised flexural tests on three types of full-scale RC beams with T-shaped cross-section: (i) two reference un-strengthened beams, (ii) two CFRP-strengthened beams previously loaded and cracked, and (iii) two CFRP-strengthened beams, previously loaded, cracked and repaired with epoxy resin. The repair and strengthening techniques consisted of respectively injecting the cracks with epoxy resin and applying CFRP laminates according to the externally bonding reinforcement technique. In the numerical study, the structural response of all beams tested was simulated using the finite element software Atena, which features a smeared cracked model constitutive relationship for concrete. A parametric study was carried out in which the influence of material parameters, namely the fracture energy, on the beams structural response was assessed. Experimental results showed that repairing cracks by means of epoxy injection before strengthening them with CFRP laminates provided a considerable increase of stiffness, but only a slight increase of ultimate strength, as failure was triggered by the debonding of the strengthening system at the anchorage zones. In the numerical study a very good agreement with experimental data was obtained. For the repaired and strengthened beams, such agreement was obtained by increasing concrete’s fracture energy when compared to that of the reference beams.
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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) | 0.001 | 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 | 0.001 | 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".