Optimization of CWP-BFS Blended Geopolymer Concrete Using BWMbased Taguchi Method
Bibliographic record
Abstract
The feasibility of blending ceramic waste powder (CWP) with blast furnace slag (BFS) to produce geopolymer concrete has seen limited investigation.This study aims to optimize the mixture proportions of CWP-BFS blended geopolymer concrete for superior mechanical and durability characteristics using the best-worst method (BWM) for multi-criteria optimization.The design of the experiments was carried out using the Taguchi method for five factors, each having four levels.The resulting CWP-BFS geopolymer concrete mixtures of the L16 orthogonal array were proportioned using different binder content, BFS replacement percentage, alkaliactivator solution to binder ratio (AAS/B), sodium silicate (SS) to sodium hydroxide (SH) ratio (SS/SH), and SH solution molarity.Test methods included compressive strength and water absorption.The two quality criteria were given equal weights to determine the optimal levels of factors.The method revealed that the optimum mix had a binder content of 450 kg/m 3 , BFS replacement percentage of 60%, AAS/B of 0.5, SS/SH of 1.5, and SH solution molarity of 10 M. Experimental findings endorse the utilization of CWP in geopolymer concrete as a means of alleviating the adverse environmental impact associated with its disposal.
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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 | 0.000 |
| 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 | 0.001 | 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".