Mechanical Performance of Concrete with Graphene Oxide: Evaluation of Compressive and Splitting Tensile Strength
Bibliographic record
Abstract
Concrete is currently one of the most widely used materials in the world, but its production involves the emission of significant amounts of CO2 due to cement manufacturing.To address this global issue, various materials and methods have been explored.In this context, the incorporation of graphene oxide (GO) into concrete emerges as an innovative solution due to its remarkable physical and chemical properties.GO has been shown to enhance the mechanical properties of concrete, allowing target strength to be achieved with less cement.Therefore, this study aims to evaluate the influence of GO on the mechanical properties and workability of concrete.Four concrete mix designs were evaluated, consisting of a control group and three dosages (0.03%, 0.05%, and 0.08% GO relative to the cement weight).Tests were conducted for compressive strength, split tensile strength, and slump.Results indicate that the 0.08% dosage proved to be optimal, with a 22.71% increase in compressive strength and a 23.37% increase in split tensile strength at 28 days compared to the control mix.However, this dosage exhibited the lowest slump, measuring 139 mm.In conclusion, the incorporation of GO into concrete improves its strength, particularly at early stages, suggesting its potential for structures subjected to heavy loads from the outset.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".