Performance evaluation of high-performance self-compacting concrete with waste glass aggregate and metakaolin
Bibliographic record
Abstract
High-Performance Self-Compacting Concrete (HPSCC) has attracted much attention in recent decades due to its remarkable ability to fill formworks with densely packed reinforcing bars while requiring minimal or no external compaction. Because of the negative environmental impacts of cement and natural aggregates in concrete production, a much more sustainable alternative to manufacturing HPSCC is required. Recycled glass waste is one of the most attractive waste materials that can be used to create sustainable concrete compounds, which is currently a major area of study among researchers. This study aims to develop information not only about the fresh, mechanical, and durability characteristics of HPSCC, evaluate the environmental impact and correlate the crushing strength using a non-destructive approach by utilizing waste glass aggregates at replacement percentages of 0%, 10%, 20%, 30%, and 40%. To improve the performance of the produced HPSCC, Metakaolin was also added. The results of the fresh concrete tests revealed that the substitution of an optimal level of waste glass with Metakaolin provides adequate implementation in flowability , passing ability, and viscosity behaviors. Even though there is a reduction in the mechanical performance with glass aggregates, Metakaolin significantly improved strength and ductility by up to 16.12% and 15.91%, respectively. Furthermore, in most cases, the use of glass aggregates with Metakaolin significantly alters the durability properties of concrete while minimizing the environmental impact as well as the overall project cost. Finally, the NDT assessment demonstrates that the analytical equation can efficiently predict the compressive strength and promising to use for field application.
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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) | 0.000 | 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".