Eccentrically loaded corroded RC columns repaired with advanced composites: Experimental testing and analytical modeling
Bibliographic record
Abstract
This study provides direct experimental evidence comparing carbon fiber-reinforced polymer (C-FRP) and carbon fabric-reinforced cementitious matrix (C-FRCM) systems for rehabilitation of short reinforced concrete (RC) columns. Fifteen RC columns were tested under eccentricity-to-depth ratios ( e/h ) of 0.0–0.3. Corroded columns were pre-damaged through accelerated corrosion, resulting in steel losses of 22% in longitudinal bars and 42% in ties. Corrosion reduced the load capacity by 41% under concentric loading and by an average of 17% under eccentric loading. Both repair systems effectively restored the load capacity of the corroded columns. C-FRP repairs increased the load capacity by 80–167%, while C-FRCM achieved load capacity gains of 49–86%. The lower effectiveness of C-FRCM was ascribed to a premature debonding at the fabric–mortar interface. A new analytical model was developed to predict the load capacity, incorporating material nonlinearities, corrosion-induced degradation, and combined confinement from internal steel ties and external composite wraps. Model predictions were validated using experimental results from this study and additional literature data. The model produced P–M interaction diagrams consistent with experimental trends, confirming its reliability and practical use as a simple, accurate tool for structural evaluation and retrofit design.
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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 | 0.001 | 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) | 0.001 | 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".