Bibliographic record
Abstract
Different initiatives have evolved over the years to improve the durability of concrete, and one of the promising areas gaining attention in recent years is the use of nanomaterials in concrete. Though most of the applications of nanomaterials to improve the properties of concrete has been restricted to laboratory applications, it is anticipated that in few years to come more commercial and large-scale applications will ensue. This overview explored different types of nanomaterials already used in concrete and their effects on the durability of concrete. It was found out that nanosilica is the most used nanomaterial in concrete. And all types of nanomaterials currently used, enhance the durability of concrete significantly compared to other methods employed before the advent of nanomaterials in concrete. However, the use of other nanomaterials such as nanotitania and nanoalumina is attracting great attention. But the use of nanomaterials in concrete is faced by several challenges such as its high cost, production process, toxicity, etc. It is expected that with more research and application in the use of nanomaterials to enhance the properties of concrete, cheap and user-friendly nanomaterials can be developed. In addition, this review shows the possibility of enhancing the current durability properties with the use of nanomaterials.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".