Improving nonlinear behavior and tensile and compressive strengths of sustainable lightweight concrete using waste glass powder, nanosilica, and recycled polypropylene fiber
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%.
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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".