Corrosion-Damaged RC Beams Repaired with Fabric-Reinforced Cementitious Matrix
Bibliographic record
Abstract
The structural performance of corrosion-damaged reinforced concrete (RC) beams repaired with fabric-reinforced cementitious matrix (FRCM) was investigated. Eleven RC beams were constructed and tested in flexure under four-point load configuration. Nine beams were subjected to an accelerated corrosion process for 70 days to obtain an average mass loss of 13% in the tensile steel reinforcing bars while two other beams were tested as controls. One corroded beam was repaired with carbon fiber-reinforced polymer (CFRP) before testing for comparison. The test parameters included the number of fabric plies (1–4), the FRCM repair scheme (end-anchored and continuous U-wrapped strips), and FRCM materials [carbon and polyparaphenylene benzobisoxazole (PBO)]. Test results showed that corrosion slightly reduced the yield and ultimate strengths of the beams. The use of FRCM increased the ultimate capacity of corroded beams between 5 and 52% and their yield strength between 6 and 22% of those of the uncorroded virgin beam. Beams repaired with U-wrapped FRCM strips showed higher capacity and higher ductility than those repaired with the end-anchored bottom strips having a similar number of layers. A high gain in the flexural capacity and a low ductility index were reported for specimens with a high amount of FRCM layers. A new factor was incorporated in the design equations of the ACI 549.4R-13 to account for the FRCM scheme.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".