Overview of trends in the application of waste materials in self-compacting concrete production
Bibliographic record
Abstract
Self-compacting concrete (SCC) production is growing rapidly due to its several advantages in terms of enhanced properties and applications. However, there are associated sustainability issues with the SCC materials. With the demand for SCC expected to continually increase, an ideal solution is therefore required to sustain the technology, such as derivation of alternative materials. Thus, this study explores innovative application of industrial wastes in self-compacting concrete production, with the aim of finding the most appropriate technique in SCC material use. Also, the potential limitations in using some of the waste materials as sustainable alternatives were highlighted. This study found that several materials emanating from industrial rejects have been mostly investigated as a potential material for making SCC, which showed that the incorporation of waste materials into SCC could be a viable approach. However, in order to achieve optimal performance of SCC, an adequate material composition is necessary. It is clear from this study that factors such as embodied carbon, energy and cost of SCC production can notably be reduced with the incorporation of waste materials. The study also identified areas for further investigations that can help in the improvement of SCC for construction 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".