Properties of Mortars Incorporating Binary and Ternary Blends of Palm Ash, Silica Fume, and Metakaolin
Bibliographic record
Abstract
In this study, we investigated experimentally and statistically this research explores the use of recycled materials (MK, SF, and PA) as partial replacements for cement in concrete and mortar production.These supplementary cementitious materials (SCMs) offer a sustainable alternative while potentially improving mechanical properties and microstructure.The study investigates the effects of binary and ternary SCM substitutions (0-25% by weight) on fresh and hardened concrete properties.A constant water-to-cement ratio (0.485) and superplasticizer dosage (0.5% of cementitious materials) were maintained.Results showed variations in the impact of SCMs on workability (slump flow).While palm ash increased slump flow, SF and MK decreased it.Laboratory testing revealed that SF replacements led to the highest compressive and splitting tensile strengths at 7 and 28 days, often exceeding the control mix.Binary blends with 20% SF and 5-10% MK or PA displayed promising strength improvements.Among ternary blends, 10% SF with 10% PA or 10% MK with 10% PA offered the best results.Interestingly, consistent strength gains were observed with varying replacement ratios for other SCMs when palm ash remained constant at 5%.Overall, the study suggests an optimal replacement level of 10% MK and 5% palm ash.These findings emphasize the potential of SCMs like MK, SF, and PA as sustainable cement replacements in concrete production, highlighting the importance of optimizing replacement levels and mix designs for desired performance and environmental benefits.The level of importance of these parameters on slump flow and compressive and splitting tensile strength was determined by using the analysis of variance (ANOVA) method.
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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".