Mechanical Properties of Concrete with Silica Fume and Coarse Recycled Aggregate Concrete
Bibliographic record
Abstract
Recently, there has been an increasing trend toward the use of sustainable materials. Sustainability helps the environment by reducing the consumption of non-renewable natural resources. Concrete – the second most consumed material in the world after water – uses a significant amount of non-renewable resources. As a result, an experimental investigation was conducted to study the hardened properties of concrete constructed with 20% and 50% recycled concrete aggregate (RCA) (coarse) as well as 6% silica fume. This experimental program consisted of six mix designs. Experimental results showed that the compressive and tensile strength of concrete improve by using recycled concrete aggregate (RCA) (coarse) and silica fume in the mix design. For example, the compressive strength of concrete containing 6% silica fume and 20% recycled concrete aggregate at age of 56 days was 14% greater than reference concrete mix, while with increasing percentage of recovered concrete aggregate by 50%, this parameter increased by only 4% which shows the inverse effect of increasing the amount of recovered concrete aggregate on compressive strength. The hardened properties of concrete were compared with the provisions of the international design codes (U.S., Australia, Canada, Europe, and Japan) as well as a database of conventional concrete. Results showed that the RCA has superior hardened properties compared with the reference concrete mix.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".