Shear strength characterization and sustainability assessment of coal bottom ash concrete
Bibliographic record
Abstract
The present study investigated the synergistic influence of coal bottom ash (CBA) on the shear strength of concrete. CBA was milled for 2, 6, and 10 h to form grinded CBA (GCBA). “L” shaped specimens were prepared with 10%–30% GCBA and 25%–50% CBA as alternative of Portland cement and natural fine aggregates. Concrete containing 20% GCBA (grinded for 6 h) and 25% CBA reported the highest shear strength owing to pozzolanic reactiveness and filler action. X-ray diffraction, scanning electron microscopy–energy-dispersive spectroscopy and Fourier transform infrared also supported the experimental outcomes. Well fitted mathematical models were derived followed by optimization using desirability function approach recommending 5.71 h of grinding, 26.27% GCBA, and 36.69% CBA as the optimum amount for its successful utilization in concrete. This approach further leads to significant reduction of about 22% in carbon footprints and eco-costs in comparison to conventional 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".