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Record W4405135230 · doi:10.46326/jmes.2024.65(6).08

Potential using of marine sand in coastal areas of Vietnam to create concrete for rural roads

2024· article· en· W4405135230 on OpenAlexaff
Nu Thi Nguyen, Son Truong Bui, Dung Ngoc Nguyen, Phong Tuc Vu, Anh Tuan Bui, Huang Xuan, Nelson Chu

Bibliographic record

VenueJournal of Mining and Earth Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsCompressive strengthGeotechnical engineeringEnvironmental scienceGeologyMaterials science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.266
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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