Characterization of Mechanical Properties of Concrete Recycled Ceramic and Glass Powder Exposed to Elevated Temperatures
Bibliographic record
Abstract
Systematic reuse of industrial debris is a crucial component that helps shape the sustainable construction system and green technology. The effective optimization of waste ceramic and glass fines into concrete mixes, as partial replacements of natural sand by volume, has been used in this study to explore the mechanical properties of ceramic recycled aggregate (CRA) and glass recycled aggregate (GRA) concrete at higher temperatures. The study comprises 17 types of concrete mixtures comprised of normal concrete (NC) along with 8 different mixes from both GRA and CRA concrete. In both types of GRA and CRA concrete, the sand replacement (by volume) ratios are similar. This paper highlights NC along with the volumetric replacements of sand as 5%, 10%, 15%, 20%, 25%, 30%, 35%, and 40% in other mixes. A total of 306 cylinders were made whereas 18 cylinders for NC and each group (GRA and CRA) included n=18 cylinders. Selected temperatures were 25°C, 100°C, 200°C, 400°C, 600°C, and 800°C to determine the overall mechanical and chemical alterations in NC and recycled concrete. The study reveals that increasing the addition of recycled glass and ceramic fines improves the overall compressive strength, and tensile strength compared to normal concrete. Higher replacement of ceramic and glass fines reduces the cracks and enhances the durability of concrete. In addition, more strength reduction was noticed in NC with increasing temperatures, while the reduction rate was slower in both GRA and CRA concrete. Furthermore, the study expounds that, by exploiting the ceramic and glass wastes (as fines) into concrete would result in two-way environmental advantages. One is, it would reduce the hazardous ceramic and glass landfills while the other is, it would minimize the frequency of sand mining.
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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".