Enhancement of Mechanical Properties of Concrete Using Industrial Waste
Bibliographic record
Abstract
In response to the evolving global landscape, there is a growing inclination towards embracing sustainable and environmentally conscious construction practices to meet the demand for more eco-friendly and climate-resilient built environments.In recent time several SCM (Supplementary Cementitious Material) had been employed in concrete for its property enhancement as well as reducing negative impact of waste on environment.Taking a step in the similar direction the present study employs Rice husk ash (RHA) and Waste marble powder (WMP) for strength enhancement of concrete.Varying percentage of Rice Husk Ash (0%, 2.5%, 5%, 10%, 12.5%, 15%&20%) and Waste Marble Powder (0%,2.5%,5%,10%, 12.5%, 15% & 20%) were used as a replacement of cement in binder.Further a combined replacement of RHA and WMP was used to prepare data cases for replacement of cement in concrete.Five different cases were designed with keeping RHA percentage constant for single case while varying the WMP percentage for same.Case 0 with no replacement, Case 1 with 2.5% RHA along with varying percentage of WMP, Case 2 with 5% RHA along with varying percentage of WMP similarly Case 3 with 10% RHA along with varying percentage of WMP and Case 4 with 12.5% RHA along with varying percentage of WMP.The varied percentage of WMP were 5%, 10%, 15% and 20% for each case.This resulted in identification of combined effect of both materials on concrete strength
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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) | 0.002 | 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".