A comparison study between normal concrete and self-compacting concrete with copper slag and steel fibres
Bibliographic record
Abstract
This study explores and compares the performance of two concretes: Normal Concrete (NC) and Self-Compacting Concrete (SCC). It focuses on using copper slag (CS) as a partial replacement for sand and adding steel fiber to improve strength. A total of 16 concrete mixes were created: eight SCC mixes (M1 to M8) and eight NC mixes (M10 to M40) with copper slag replacing sand in amounts from 0% to 100% and different types of steel fiber added. The results showed that self-compacting concrete with copper slag had excellent flow characteristics, achieving a slump flow of 690 mm without any segregation. At this level, the compressive strength increased by 9%, from 60.8 MPa to 65.73 MPa. However, using 100% copper slag reduced the strength to 49.72 MPa, a 20% decrease. At the 30% replacement level, the flexural strength improved by 4.5%, and the split tensile strength improved by 3%. In the normal concrete mixes, adding 0.5% of crimped steel fiber (aspect ratio 53.85) gave the best result, increasing the compressive strength by up to 18.16% compared to concrete without fiber. This fiber also helped control cracks, improving both tensile and flexural strength. Overall, the best performance in both SCC & NC was observed when 30% of sand was replaced with copper slag. Self- compacting concrete had better workability and could compact itself without vibration, while NC with steel fibers showed better resistance to cracking. These findings support the use of industrial byproducts and fibers to make concrete more sustainable, durable, and structurally efficient.
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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 | 0.001 | 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".