Comparative Study Involving Effect of Curing Regime on Elastic Modulus of Geopolymer Concrete
Bibliographic record
Abstract
Geopolymer Concrete (GPC) as a cement-less construction material has attracted worldwide attention due to its lower carbon footprint. There are numerous studies reported on GPC made using different by-products including fly-ash. However, since the use of bottom-ash is comparatively limited, making potassium-based GPC using this waste can be an alternative to Portland Cement Concrete (PCC). In this study, two methods of accelerated curing were used to determine the influence of elevated temperature on the compressive strength of GPC, composed of 50% bottom-ash and 50% fly-ash. GPC specimens were cured using various temperatures including ambient, 30 °C, 45 °C, 60 °C, and 80 °C for 24 h, all followed by 28 days of ambient curing. The highest compressive strength was obtained with steam curing at a temperature of 80 °C for a duration of 24 h. It is of great significance to evaluate elastic modulus of the concrete mixture so that the short-term rigidity of structures subjected to elongation, bending, or compression can be predicted. In this study, a longitudinal Resonant Frequency Test (RFT) as a non-destructive test (NDT) was used to calculate the elastic modulus of both GPC and a comparative PCC mix. Based on the results, PCC had higher resonant frequency (by about 1000 Hz) compared to GPC. A review of empirical models for predicting GPC’s elastic modulus showed that all of the predicted elastic modulus values were lower than experimental values.
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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) | 0.001 | 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".