Role of synthetic fibers in delaying steel corrosion cracks and improving bond with concrete
Bibliographic record
Abstract
An experimental study was conducted to investigate the bond behavior between corroding reinforcing steel and surrounding synthetic fiber reinforced concrete. Pullout concrete and fiber reinforced concrete specimens were prepared at concrete cover to steel bar diameter ratios of 3.1 and 3.7 with three embedded lengths of 100, 200, and 300 mm. Fiber reinforced concrete was prepared using polypropylene at 0.15% and 0.30% or nylon fibers at 0.3% by mix volume. After moist curing for 90 days, pullout specimens, originally contaminated by up to 11 kg/m3 NaCl, were subjected to a special treatment to accelerate corrosion in steel. The bond stressslippage relationship was evaluated at different corrosion levels. The findings indicated that the use of fibers delayed initiation of cracking, due to steel corrosion, and improved the ultimate bond strength at cracking and post-cracking stages. The percentage improvement in ultimate bond strength at the latter stages reached as high as 52% and 87%, respectively. It was noticed that, prior to cracking, corrosion of steel bars resulted in an increase in bond strength, regardless of the concrete cover to bar diameter ratio used, or whether fibers were used or not. After cracking (due to steel corrosion), pullout specimens prepared with polypropylene fibers showed better resistance than that of corresponding ones with nylon fibers.Key words: fibers, reinforced concrete, corrosion, cracking, bond strength, slippage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".