Optimization of CF-GP Blended Electrically Conductive Concrete
Bibliographic record
Abstract
The feasibility of blending carbon fiber (CF) with graphene powder (GP) to produce electrically conductive concrete has seen limited investigation.This study aims to optimize the mixture proportions of CF-GP blended electrically conductive concrete for superior durability and mechanical characteristics using the Taguchi optimization approach.The design of the experiments was carried out considering four factors, each having three levels.The resulting CF-GP blended electrically conductive concrete mixtures of the L9 orthogonal array were proportioned using different cement content, water-to-cement ratio, volume of ECM, and combination of ECM.Test methods included electrical conductivity and compressive strength.The two quality criteria were given equal weights to determine the optimal levels of factors.The method revealed that the optimum mix for electrical conductivity had a cement content of 400 kg/m 3 , w/c of 0.55, ECM volume of 6%, and ECM combination of 40/60, whereas for compressive strength it had a cement content of 300 kg/m 3 , w/c of 0.50, ECM volume of 2%, and ECM combination of 30/70.Experimental findings endorse the utilization of CF and GP in concrete as a means of improving the electrical performance of concrete.
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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".