Optimizing the Mix Design of Cementitious Composites Incorporating Volcanic Ash by Taguchi-TOPSIS Method
Bibliographic record
Abstract
This research investigates the potential of using volcanic ash (VA) as a partial replacement for cement in construction applications that require superior early-age properties.The study employs Taguchi methodology to optimize the cementitious composites by varying key factors, including binder content, water-to-binder ratio, dune sand substitution rate of natural sand, VA substitution rate of cement, and superplasticizer (SP) dosage.As each factor had four corresponding levels, an L16 orthogonal array was developed.Testing included the flow, setting time, and 1-day compressive strength.The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed to optimize for the three factors simultaneously.The results indicate that the optimum mix consisted of a binder content of 400 kg/m 3 , a water-to-binder ratio of 0.5, a dune sand substitution of 20%, a VA substitution of 20%, and an SP dosage of 0.75%.Accordingly, the optimum mix achieved a flow of 115 mm, a final setting time of 350 min, and a 1-day compressive strength of 18.1 MPa.This study demonstrates the optimum use of volcanic ash as a sustainable alternative to cement in construction applications with time-critical requirements.
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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 | 0.000 | 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".