Recommendations for Recycling, Processing and Reuse of Concrete
Bibliographic record
Abstract
Some countries started to recycle concrete materials for reuse in structural or other issues. Some of them, like Germany, Australia and Canada have established their own recommendation guide for recycling concrete [1,2]. The recycling consists of crushing old concrete into aggregates, and then processing it into new mixture using recycled aggregates with specified sizes [3,4]. The aim of this recycling is to save nature from deforestation and dryness, by reducing the need to gravel and so the quarries work, and also to economize the waste management [5,6]. The present research work consists of an experimental study assessing the impact of using recycled aggregates on the concrete behavior and on the country’s economy. We are especially interested in determining the best composition for the new mixture of concrete resulting from reusing different types of recycled aggregates. Different types of tests have been done depending on the aggregates sizes, their origin and their state (burned or safe). The analysis is based on the comparison between compressive strength, water-cement ratio, slump, porosity and durability. Otherwise, the impact on economy is analyzed, a priori, by studying the effect of reducing the cost of the resulting concrete on construction spending. The resulted recommendations indicate the sizes of aggregates which may constitute the best composition for recycling and processing concrete, and the best use for each type of concrete depending on behavior and economy effect.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.024 |
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".