Characterization of Hydraulic Concrete with Recycled Concrete Aggregates
Bibliographic record
Abstract
The rise of urban development has led to an increase in construction projects, resulting in the demolition of existing structures on sites designated for new construction.This demolition process generates construction waste, which becomes an environmental pollutant.In response to this issue, research has been undertaken to find solutions for waste management.One proposed alternative is the reuse of concrete waste as a replacement for natural aggregate materials such as sand and gravel in the production of hydraulic concrete.The treatment of construction waste involves a process that starts with the mechanical crushing of the material to produce smaller fragments.The recycled material is then subjected to laboratory tests to determine properties such as absorption, specific gravity, and bulk density.Test cylinders are prepared by substituting 33% of the natural aggregate with recycled aggregate, followed by tests with 66% recycled aggregate, and finally, 100% recycled aggregate.Additionally, research is conducted on the use of fine recycled aggregates in mortars, developing mortar cubes with the same principle of replacing natural aggregate with recycled aggregate in one-third increments.As an added value to the work, the same tests are performed for different mix combinations, with the inclusion of additives to evaluate their effect on the behavior of recycled concrete.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".