RETRACTED: A study on the mechanical performance, shrinkage and morphology of high-performance fiber reinforced concrete with varying SCMs and geometry of steel fibers
Post-publication record
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Bibliographic record
Abstract
This paper investigates the effects of silica fume (SF) and metakaolin (MK) as cement substitutes on the mechanical properties, shrinkage, and toughness performances of steel fiber reinforced concrete (SFRC). Initially, a reference concrete mix with a water-to-binder ratio of 0.4 is blended with different volume fractions of steel fibers with varying geometries (crimped steel and straight steel), both individually and in combination, to examine their mechanical properties. Also, the possible influence of pozzolans on the variation of drying shrinkage and flexural toughness of hybrid steel fiber reinforced concrete (Hy-SFRC) was evaluated. An increase in workability was observed as a result of hybridization of steel fibers. Pozzolanic steel fiber reinforced concrete (SFRC) exhibited a more significant enhancement in compressive strength and flexural strength compared to non-pozzolanic SFRC. The hybrid combination of CS 1.5 % and SS 0.5 % was found to be the best in terms of mechanical properties. The addition of SF and MK reduced the shrinkage strain by up to 50 % compared to the reference mix. The flexural toughness values for both binary and ternary pozzolanic Hy-SFRC were notably higher than those for non-pozzolanic Hy-SFRC, indicating a stronger bond between the fibers and the matrix. Hy-SFRC containing a ternary pozzolanic mix of SF 10 % and MK 10 % gave the best results in flexural toughness. The results were consistent with morphology analysis, which revealed an increase in hydration products at the interface between the aggregate and concrete matrix, as well as between the steel fiber and concrete matrix, due to the ternary blending of SF and MK.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".