Using Slag in Manufacturing Masonry Bricks and Paving Units
Why this work is in the frame
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Bibliographic record
Abstract
The iron and steel industry is, unfortunately, a notorious generator of waste. In Egypt, 136,000 imperial tons (600,000 metric tons) of Blast Furnace Slag (BFS), 68,000 imperial tons (300,000 metric tons) of Electrical Arc Furnace Slag (EAFS), and 45,000 imperial tons (200,000 metric tons) of Basic Oxygen Furnace Slag (BOFS) are generated annually. Such slag is not only hindering the use of land for more useful purposes, but it is also contaminating it. This paper introduces green construction materials, whereby different slag types are proposed to replace coarse aggregates in producing cement masonry bricks and paving interlock units. Three different slag replacement levels were investigated, namely: 33%, 67%, and 100%. Masonry bricks were tested for bulk density, water absorption, compressive strength, and flexural strength. Paving interlock units were examined for bulk density, water absorption, compressive strength, and abrasion resistance. Heavy metals content and water leaching tests were also conducted for all slag types under investigation to assess the health impact of the proposed utilization. The test results revealed that slag masonry bricks exhibited higher strength than the control group and fulfilled the ASTM strength requirements for both non load-bearing and load-bearing walls. All slag types resulted in paving interlock units having higher compressive strength than the control mix and much lower abrasion coefficient than the ASTM limit. The results of heavy metals content and water leaching tests showed that all slag types can be safely used in the proposed field of applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it