Use of Taguchi Method to Optimize the Mix Design of Pervious Geopolymer Concrete
Bibliographic record
Abstract
The challenges related to the impervious pavement, including tire-pavement noise, urban heat island effect, and skid resistance, can be mitigated using pervious pavements.Instead of cement, Pervious geopolymer concrete (PGC) utilizes industrial wastes that would otherwise end up in landfills to produce an alkali-activated binder.This paper aims to optimize the mix design of PGC for superior mechanical performance using the Taguchi method.The binder consisted of a 3:1 blend of ground granulated blast furnace slag and fly ash.A total of nine PGC mixtures were designed, considering four factors, each at three levels, namely the binder content (400, 450, 500 kg/m 3 ), dune sand addition (0, 10, 20%), alkaline activator solution-to-binder ratio (AAS/B, 0.55, 0.60, 0.65), and sodium hydroxide (SH) molarity (8,10,12).The signal-to-noise (S/N) ratios were determined to optimize the mixture proportions for superior mechanical performance, with compressive strength being the response criterion.Results showed that the optimum mix was made with a binder content of 500 kg/m 3 , dune sand addition of 20%, AAS/B of 0.60, and SH molarity of 12 M. Experimental research findings serve as a guide for optimizing the production of PGC with superior compressive strength while minimizing the number of experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".