Macro- and microstructural behavior of graphene-nanoplatelet-reinforced cement composite
Bibliographic record
Abstract
This article focuses on establishing the suitability of graphene-nanoplatelet (GNP) cement composite to reduce the steel reinforcement percentage in concrete constructions. The nanofiber percentage in the composite is derived based on the developed compressive and flexural strength. The GNP is mixed with cementitious material with various percentages (0.01%–0.3%) to observe the optimum percentage. After getting the optimum percentage of GNP with cement, comprehensive research was conducted on the composite’s mechanical, morphological, rheological, and optical properties. The numerical study has been conducted based on representative volume elements (RVE) that simulate the composite material. The RVE represents the yielding value of the entire volume, considering the minimum volume upon which a measurement can be made. The research delivers essential information on developing a GNP-reinforced cement composite with prescribed nanofiber percentages that can confidently be adopted to reduce the rate of steel reinforcements for any concrete construction.
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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".