Characteristics of Blended Geopolymer Concrete Using Ultrafine Ground Granulated Blast Furnace Slag and Copper Slag
Bibliographic record
Abstract
This paper presents the properties of blended geopolymer concrete manufactured using fly ash and ultrafine Ground Granulated Blast Furnace Slag (UFGGBFS), along with the copper slag (CPS) as replacement of fine aggregate (crushed stone sand). Various parameters considered in this study include different sodium hydroxide concentrations (10M, 12M and 14M); 0.35 as alkaline liquid to binder ratio; 2.5 as sodium silicate to sodium hydroxide ratio and cured in ambient curing condition. Further, geopolymer concrete was manufactured using fly ash as the prime source material which is replaced with UFGGBFS (0%, 5%, 10% and 15%). Copper slag has been used as replacement of fine aggregate in this study. Properties of the fresh manufactured geopolymer concrete were studied by slump test. Compressive strength of the manufactured geopolymer concrete was tested and recorded after curing for 3, 7 and 28 days. Microstructure Characterization of Geopolymer concrete specimens was done by Scanning Electron Microscope (SEM) analysis. Experimental results revealed that the addition of UFGGBFS resulted in an increased strength performance of geopolymer concrete. Also, this study demonstrated that the strength of geopolymer concrete increased with an increase in sodium hydroxide concentration. SEM results revealed that the addition of UFGGBFS resulted in a dense structure.
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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".