The Effect of Steel Fiber and Internally Curing on the Strength of Self-Consolidated Concrete
Bibliographic record
Abstract
The main idea of this study is to find the effect of steel fiber on the strength and internally curing of self-consolidated concrete (SCC), by using lightweight aggregate (LWA) from available porcelain. The work includes two stages; the first stage involved making several experimental mixes and then choosing the one that corresponds to international standards with natural properties. The second stage was adding lightweight aggregate (LWA) by replacing 15% of sand with saturated fine lightweight aggregate (LWA) as internal curing material to study the change in the Mechanical properties of SCC. Four concrete mixes were used with different volume fractions of hooked steel fibers were incorporated 0%, 0.5%, 1%, and 1.5%. Results showed that adding steel fibers provides a slight increase in compressive strength while significant enhancement in tensile properties was observed. Furthermore, replacement of fine aggregate by (LWA) causes an increase in hydration which leads to higher compressive and tensile strengths. Results of the rate of absorption indicate that adding steel fibers has beneficial effects.
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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".