Development of a Lightweight Low-Carbon Footprint Concrete Containing Recycled Waste Materials
Bibliographic record
Abstract
Use of any recycled material helps to maintain a greener environment by keeping waste materials out of the landfills. Recycling practices also can decrease the environmental and economical impact of manufacturing the materials from virgin resources, which reduces the overall carbon footprint of industrial materials and processes. This study examined the use of waste materials such as crushed glass, ground tire rubber, and recycled aggregate in concrete. Compressive strength and elastic modulus were the primary parameters of interest. Results demonstrated that ground tire rubber introduced significant amounts of air into the mix and adversely affected the strength. The introduction of a defoamer was able to successfully remove part of the excess air from the mix, but the proportional strength improvements were not noted implying that air left in the defoamed mixture had undesirable characteristics. Freeze-thaw tests were next performed to understand the nature of air in the defoamed mixtures, and results demonstrated that this air is not helpful in resisting freeze-thaw resistance either. Overall, while lightweight, low-carbon footprint concrete materials seem possible from recycled materials, significant further optimization remains possible.
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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".