Strength, Microstructure, and Life Cycle Assessment of Silicomanganese Fume, Silica Fume, and Portland Cement Composites Designed Using Taguchi Method
Bibliographic record
Abstract
The incorporation of supplementary cementitious materials (SCMs) into cementitious materials can be used to offset the overall carbon footprint of cement in addition to improving performance and promoting circular economy. Synthesized silicomanganese fume (SiMnF), silica fume (SF), and ordinary portland cement (OPC) based binary and ternary cementitious mortar specimens were designed and optimized using the Taguchi method. Four factors with three levels each were investigated—SiMnF content of 0%–40% and SF content of 0%–10% (by mass) of the total cementitious, sand-to-binder of 1.5–2.5, and water-to-binder ratio of 0.35–0.45. Based on the orthogonal array proposed by the Taguchi method, nine mortar mixes were batched and their flow after mixing and compressive strength at 3, 7, and 28 days of casting were measured. The strength data were statistically analyzed using ANOVA to investigate the effects of the chosen experimental variables. It was observed that the strength is considerably reduced from the addition of SiMnF, but the reduction is marginal from increasing the sand-to-binder ratio. The addition of 5% SF increased the strength. A restricted analysis indicated that specimens prepared with 20% SiMnF, or 20% SiMnF and 5% SF can yield mortar strengths of up to 30.5 MPa and 48.8 MPa, respectively. Microstructural investigations revealed that the mixes with SiMnF have detectable pores at 1,000× magnification, however, the addition of 5% SF densifies the matrix with no visible pore at the same magnification. This corroborates the strength data. The life-cycle assessment (LCA) indicates that the utilization of SiMnF in the mortar mixtures can reduce CO2 emissions by up to 25% at a reasonably acceptable compressive strength.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".