Optimization of CWP-Slag Blended Geopolymer Concrete using Taguchi Method
Bibliographic record
Abstract
This paper aims to optimize the mixture proportions of geopolymer concrete prepared using a binary binder system composed of ceramic waste powder (CWP) and ground granulated blast furnace slag (or simply slag) for superior mechanical performance.The corresponding mixtures were proportioned, analyzed, and optimized by adopting the Taguchi approach.The binder content, CWP replacement rate by slag, alkali-activator solution-to-binder (AAS/B) ratio, sodium silicate-to-sodium hydroxide (SS/SH) ratio, and sodium hydroxide solution molarity were assigned as factors in the design phase.Each factor was characterized by four different levels, resulting in the establishment of an L 16 orthogonal array.The target design property was the 28-day cylinder compressive strength.The analysis of variance showed that AAS/B ratio, CWP replacement rate by slag, and SS/SH ratio were key factors affecting the strength in geopolymer concrete, while SH molarity and binder content showed the least contributions.The blended geopolymer made with 40% CWP and 60% slag yielded the optimal compressive strength response with a binder content, AAS/B ratio, SS/SH ratio, and SH solution molarity of 450 kg/m 3 , 0.5, 1.5, and 10 M, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".