Enhancing of Concrete Properties by Using Aluminium and Iron Residues as a Partial Replacement of Fine Aggregate
Bibliographic record
Abstract
According to the growing of the world's population, the necessitate for using different building materials such as cement, steel, admixtures, wood and aluminium increased. The main problem faces the world with the increasing need for building materials is the construction wastes, which are a type of environmental pollution that must be reduced. This study focused on aluminium and iron wastes resulted from factories and the possibility to reuse these wastes again like partial replacement from sand with proportions (0.25, 0.5, 0.75 and 1%) in concrete mixtures for both types of residues at ages (7, 14, and 28) days. It had studied the effect of the using of aluminium residues (AR) and iron residues (IR) especially on the mechanical characteristics of concrete. These mechanical characteristics were the compression strength (CS) and tensile strength (TS). The achieved results were compared between the residues concrete specimens and those of reference concrete. Also, the effect of age and replacement percentage for samples which they contain AR and IR had been discussed. The results had shown that the (CS) of iron residues concrete samples (IRC) was increasing with increasing of the iron residues percentage till (0.5%) (IR) in opposite of the observed values of aluminium residues concrete (ARC), as well as for the results of the (TS) of concrete samples which containing the mineral residues at all ages. Where it was observed that the using of aluminium residues (AR) showed a decreasing in the values of (TS) and (CS) compare with reference concrete, but with the use of iron residues (IR) both of (TS) and (CS) results were more than their counterparts of reference concrete at all ages for the first two percentages of replacement.
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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".