Analysis of Cement Properties and Physico-Chemical Characterization of Mineral Waste: Impact of Mineral Additives on Cement Performance
Bibliographic record
Abstract
The cement industry faces growing challenges related to sustainability and cost reduction.This study explores the potential for partially replacing clinker with mineral additives such as limestone, slag, and pozzolan.Through detailed physico-chemical analyses and concrete testing, the results demonstrate that these materials can enhance certain mechanical properties while reducing the environmental impact of cement production.Experimental data suggest that mineral additives at various percentages significantly influence the compressive strength and consistency of cements.The integration of theory and experimental methodologies has enabled the study of the impact of mineral additives (limestone and slag) in varying proportions (5%, 10%, 15%, 20%) on the physicomechanical properties of cements, compared to Artificial Portland Cement (CPA).Slag significantly improves short-term mechanical strength due to its pozzolanic properties, while limestone decreases both density and mechanical strength due to its inert nature.The results indicate that the best performance is obtained with an additive dosage of 10%, providing an effective balance between consistency, density, and strength.This study provides pragmatic advice and suggestions for the incorporation of mineral additives in the development of more environmentally friendly and economically viable cements.
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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 | 0.001 |
| 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".