Modeling of Mechanical Performance from Concrete Made by Combining Iron Sand and Glass Powder Filler under Hot Water Curing Condition
Bibliographic record
Abstract
The proportions of the concrete are critical for obtaining a high-strength, high-quality product. Different concrete grades may support varying loads and have varying ratios. Therefore, the objectivity of this research is to study mechanical performance from concrete treated with iron sand, glass powder, and hot water curing. The level of iron sand and glass powder treatment added to the SNI concrete composition is 2% to 10%, 1% to 5%, (w/w), respectively. The curing time and temperature were applied between 1 hr to 4 hr and 301 K to 343 K. The compressive strength of the concrete was tested on 0, 1, 3, 7, and 28 days after molding using UTM with a combination of linear regression and response surface models for optimization. The addition of iron sand and glass powder to increase the best compressive strength was 10%, 2.45%, respectively. Iron sand treatment of 10% combined with glass powder filler 3.04% was predicted to give the best compressive strength of 32.50 MPa. The compressive strength value of this treatment was 21.78% higher than that of SNI concrete. Treat the curing temperature, curing time, and age of the concrete which gives the best compressive strength (28.33 MPa) with SNI concrete composition of 301 K, 4 days, and 28 days. This research shows that the addition of iron sand, glass powder, and hot water curing to the composition of SNI concrete can increase its compressive strength.
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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 | 0.001 |
| 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 | 0.001 | 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".