Experimental Investigation of Cement Mortar Incorporating Stone Powder and Admixtures
Bibliographic record
Abstract
Concrete is a versatile building material that finds use in many various applications.In typical conditions, it works effectively, but in extreme circumstances, it may fail as well.Admixtures can be added to cementitious materials to achieve the required properties during or after construction.Admixtures that accelerate cement composites' early age strength development and setting happen more quickly.Stone is also a significant building material used in the construction industry.Annually, a significant amount of waste is generated due to the stone industry's expansion and the building sector's growth.Stone wastes have been deposited on valuable land and watersheds in various forms such as slurry, dust/powder, broken slabs, and aggregates.It disturbs the ecology and may cause detrimental effects to the environment as a consequence.In the present investigation, the viability of using stone powder and accelerating admixtures in concrete has been investigated from both an ecological and economical aspect.This study substituted stone slurry powder for cement; non-linear regression equations were also developed, and calcium nitrate and triethanolamine were utilized as additions to examine the applicability of additives in mortar.Additionally, a cost and environmental impact study was carried out.The findings showed that stone powder was more effective in terms of strength, cost, and environmental friendliness.The specimens that were cured in water had a greater compressive strength than air-cured specimens.The optimum percentage of calcium nitrate and stone waste was 1% and 7.5% in the mortar mixes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".