Development of a Lightweight Low-Carbon Footprint Concrete Containing Recycled Waste Materials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it