Concrete compressive strength of mix proportioning cockle shell, glass powder and epoxy resin under hot water curing condition through response surface methodology
Bibliographic record
Abstract
Nowadays, the significant demand for concrete has become a problem in concrete using aggregate from waste. Using standard concrete is recommended to reduce the breakdown of buildings. Unfortunately, standard materials used to produce previous concrete are not entirely environmentally friendly. As a result, many researchers have committed their awareness to identifying eco-friendlier substitutions in manufacturing concrete substitution aggregate from waste. In this respect, this paper discussed the proposed efficient procedure to indicate the compressive strength from mixed proportioning cockle shell, glass powder, and epoxy resin as concrete under hot water curing conditions (60°C, 4 hr) using response surface methodology. The experimental design used in this research uses a response surface methodology. There are three aggregates to be investigated, namely cockle shell powder, glass powder and epoxy resin under hot water curing condition (60°C, 4 hr). Under hot water curing conditions, this research discovered that adding 4.0% cockle shell powder and 10.0 % glass powder increased the compressive strength to 104.68 MPa. On the other hand, 4.0% cockle shell powder, 10.0% glass powder and 2% epoxy resin under hot water curing conditions improved the compressive strength to 115.70 MPa. It was therefore inferred that the use of both cockle shell powder and glass powder to produce cleaner and compressive strength concrete is applicable, both mechanically and environmentally.
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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".