The influence of fine aggregate combinations on particle size distribution, grading parameters, and compressive strength of sandcrete blocks
Bibliographic record
Abstract
The current extensive use of low priced fine aggregate (sand) deposits in sandcrete block making in Nigeria is of concern because there appears to be a level of ignorance surrounding their existing properties and implications. To this end, silt contents and some grading parameters of the most commonly used fine aggregate deposits in parts of midwestern Nigeria (Benin City), the coefficient of uniformity (Cu), curvature coefficient (Cc), and the fineness modulus (Fm) were derived by laboratory experiments to ascertain these basic properties. In addition, the strength and durability properties of sandcrete blocks made from these sands were also established. It revealed that the low priced sands exhibited worse properties in comparison to the more expensive sand. As a way of improving the properties of these frequently used low priced sands, a combination approach was adopted that used the weaker and commonly used sands with those that are more expensive and less frequently used. Findings revealed that combining the two created significant improvement in compressive strength, durability, and grading parameters of low priced sands with only marginal impact on cost.Key words: fine aggregates, uniformity coefficient, curvature coefficient, fineness modulus, compressive strength, durability, silt contents, Nigeria.
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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 | 0.001 |
| 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".