Optimizing Sand Concrete Properties Through Partial Substitution of Natural Sand with Cement Kiln Dust
Bibliographic record
Abstract
This study investigated the impact of substituting ordinary sand with cement kiln dust (CKD) as sand on the rheological, mechanical characteristics and some aspects of durability of sand concrete, formulated according to the experimental method of SABLOCRETE.Concrete mixtures were prepared with different CKD replacement rates: 0%, 5%, 10%, 15%, and 20%, while the quantities of cement, water, limestone fines, and adjuvant still constant.Different concrete mixtures were tested to measure: density, workability, air content, compressive and flexural strengths.Moreover, capillarity and immersion tests were performed to evaluate water absorption, and resistance to sulfuric acid exposure was assessed to estimate chemical durability.Results demonstrate that substitution rates had a significant influence on the properties of sand concrete.The optimal mechanical performance was around 15% of CKD sand.In contrast, the highest water absorption rates (both by immersion and capillarity), were observed in the concrete with 20% of CKD sand.However, the mass loss due to sulfuric acid, was lower in concrete containing 10% CKD sand.This investigation underlined the role of CKD's proportion in determining the mechanical and durability characteristics of sand concrete, offering valuable insights for sustainable construction practices.
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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".