The Effect of Height Replacement Fly Ash on Properties of Mortar
Bibliographic record
Abstract
All the time there are lot of tons of cement compositions, including normal Portland cement, are produced around of the world, causing the pollution of atmosphere by greenhouse gas CO2.For this reason, researchers work to make production more ecofriendly, a laboratory study was aimed to use and evaluate the properties of the mortar mixture made with high dosages of fly ash as a replacement material with cement to reduce the side effect of cement.Cement was used as main binding material for mortar mixture, and fly ash was used as a replacement material at different ratios.Properties of mortars, including density, compressive and flexural strength were evaluated.Cement was replaced with fly ash, replacement dosages on mass basis were 0%, 10%, 20%, 30%, 40%, 50% and 60%.Initially, flexural strength and compressive strength measured at twenty-eight days, and sixty days.The results showed compressive and flexural strengths of mortar were improved with the replacement of fly ash.Fly ash increases at replacement level, 10%, 20%, and 30%, the compressive strength of mortar was increased by 57%, 17% and 6% respectively compared with zero fly ash at 28 days.While at 60days were 25%, 9%, and 13% of replacement level, 10%, 20%, and 30% respectively.At the same time, high percentage fly ash replacement lower strength than normal mortar However, the results shown the mortar with 10% fly ash dosage is the best content for maximum 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) | 0.001 | 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".