A State of the Art on Red Mud as a Substitutional Cementitious Material
Bibliographic record
Abstract
Red mud is a highly alkaline solid waste produced from the alumina refinery plants. Every year more than 300 million tons of red mud is producing throughout world. Disposal of a large quantity of red mud is very expensive and it creates contamination of neighbor lands, air and water bodies. Using red mud as a sustainable cementitious material in concrete is highly appreciable, because concrete is a second largest using material after water. Moreover, reduce the negative effect on environment due to red mud disposal as well as cement industries. The present paper conducted a critical review on bayer process of red mud, physical and chemical properties of red mud. And also workability, mechanical, durability and microstructure characterization of red mud when used in concrete as sustainable cementitious material. In the red mud iron oxide and alumina oxide are presented abundantly. Red mud accelerates the heat of hydration in concrete and it leads to strength enhancement in early ages. Increases the quantity of red mud in concrete reduces the workability but increases the strength of concrete. However, the chemical composition of red mud and its particle size helps to improve the durability property of concrete. Red mud offers more capable to arrest the chloride ions and other ions diffusion into concrete. Red mud minimizes the micro cracks and voids present in concrete by its particles size as well as bonding nature with other materials up to certain dosage of red mud used in concrete.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".