Effect of Replacing Cement with Factory Waste (Steel, Aluminum and Copper) on the Compressive Strength of Concrete
Bibliographic record
Abstract
The steel, aluminum and copper waste from the factories has a negative effect on the environment. This study investigates the possible reuse of steel, aluminum and copper waste as a supplementary cementitious material and its effect on concrete properties. This paper proposes a model for the prediction of the compressive strength of cement concrete and mortar as the cement was replaced with steel, aluminum and copper waste with the following by weight percentages: 0%, 5%, 10%, and 15%.The tests performed in this paper are conducted to the following samples: (50 * 50 * 50 mm) mortar samples, (150 * 150 * 150 mm) concrete cubes, and (150 * 300 mm) concrete cylinders. Tests are performed at the periods of 7 and 28 days. The results show that the use of steel waste (Fe15%) increased compressive strength of concrete by 18.5% and copper waste by (Cu15%) increased it by 15.77%, aluminum (Al15%) increased it by 12.95% and steel waste (Fe15%) increases compressive strength of cement mortar by 9.55% as compared to pure mixing. Consequently, adding steel waste to concrete gives it a compressive strength of more than the double of its original strength.
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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 | 0.001 |
| 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".