Eco-efficient preplaced recycled aggregate concrete incorporating recycled tyre waste
Bibliographic record
Abstract
This experimental study explores the development of highly eco-efficient concrete. This concrete incorporates 50% higher coarse aggregate content compared with normal concrete, thus reducing cement demand, and its granular skeleton is entirely recycled. Moreover, it adopts a unique energy-efficient placement technique whereby the granular skeleton is first preplaced in the form and then injected with a flowing grout, which considerably reduces the energy of mixing and placement. Various mixtures incorporating recycled concrete aggregate along with recycled rubber granules and steel wire fibres retrieved from scrap tyres were made. The mechanical strength and post-cracking behaviour of the eco-efficient concrete were evaluated. While tyre rubber decreased mechanical strength as expected, scrap tyre steel wire fibres enhanced the tensile and flexural behaviour, exhibiting superior energy absorption and ductility compared to the brittle failure of the control mixture. The results provide an insight into the level of recycled tyre rubber and steel wire that could be combined with recycled concrete aggregate to achieve durable and cost-effective eco-efficient preplaced recycled aggregate and rubberised concrete for sustainable non-structural applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".