Potential using of marine sand in coastal areas of Vietnam to create concrete for rural roads
Bibliographic record
Abstract
In Vietnam, there is not enough sand from the river for the construction operation, and it is necessary to find other resources to replace it. Marine sand was distributed in large areas in the coastal area of Vietnam and was not used for rural roads. This paper presents experimental results on the workability and compressive strength of concrete which was made from Nghe An and Ha Tinh marine sand. The particle size of two marine sands is not fully suitable for use as fine aggregates in concrete according to TCVN 7570:2006. The Ha Tinh sand was more suitable for use as fine aggregates in concrete than Nghe An sand. Three grades of concrete (M20, M25, M30) were designed for testing. Fifty-four samples of concrete were mixed and cured for 3, 7, and 28 days. Results showed that the compressive strength of Ha Tinh concrete achieved the requirements of M20, M25, and M30 grade - concrete. Otherwise, the compressive strength of Nghe An concrete has only achieved the requirements of M10, M15, and M20 grade – concrete. The research results also showed that the concrete made from Ha Tinh sand can be used for the A, B, C, and D grades of rural roads and the concrete made from Nghe An sand can only be used for the C, D grades of rural roads. The workability of all concrete mixtures met the requirements of Vietnamese standards. The sea sand in the coastal area of Vietnam has enormous potential to produce concrete for rural roads.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".