Effect of mix composition on workability and compressive strength of self-compacting geopolymer concrete
Bibliographic record
Abstract
Concrete performance expectation has risen to satisfy the ever increasing societal needs alongside with the advancement of technology. Self-compacting geopolymer concrete (SCGC) is an improved way of concreting execution that does not need compaction and it is made by complete elimination of ordinary Portland cement content. This paper reports results of an experimental study on workability and development of compressive strength of SCGC prepared by thermal reaction of low calcium fly ash with sodium hydroxide, sodium silicate and super plasticizer. The effects of water to geopolymer solids on fresh properties such as filling ability, passing ability and resistance to segregation were studied. The fresh properties were assessed using slump flow, V-funnel, L-box and J-ring test methods. The basic requirements for flowability and resistance to segregation for self compacting according to European Federation of National Associations Representing Producers and Applicators of Specialist Building Products for Concrete (EFNARC) were satisfied. This paper also reports the effects of curing duration and temperature on the compressive strength development. The compressive strength of 51 MPa was obtained for self compacting geopolymer concrete cured at 70 °C for 48 h with water to geopolymer solids ratio of 0.33.
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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 | 0.001 |
| 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) | 0.002 | 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".