Improving nonlinear behavior and tensile and compressive strengths of sustainable lightweight concrete using waste glass powder, nanosilica, and recycled polypropylene fiber
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
Abstract Concrete is one of the most extensively utilized building materials that can be produced, and has the potential to release a significant quantity of CO 2 into the environment. In this research, through studying lightweight (LW) concrete, attempts are made to produce environmentally friendly LW concrete with high strength using nanosilica rather than part of the cement and waste glass powder instead of aggregates. Recycled polypropylene fibers are used to increase the concrete’s compressive strength and nonlinear behavior. The use of glass powder was 20, 25, and 30% of the weight of aggregates, the consumption of nanosilica was 1, 2, and 3% of the weight of cement, and the consumption of recycled fibers (FORTA Ferro-Green) was 0.5, 1, and 1.5% of the weight of cement. Leca is also utilized as a LW aggregate. According to 7- and 28-day experimentation results and field emission scanning electron microscope analysis, the best sample had 1.5% fiber, 3% nanosilica, and 25% waste glass powder, and had a compressive and tensile strengths of roughly 1.7 and 1.6 times, respectively, those of the control specimen after 28 days. Also, using 3% nanosilica instead of cement can reduce greenhouse gas emissions by about 3%.
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