Strength and Microstructural Behavior of Sustainable Concrete with Varied Proportions of Recycled Fine Aggregate from Construction and Demolition Waste
Bibliographic record
Abstract
Recycled fine aggregates (RFAs) encompass inorganic particles derived from the crushing of building and demolition waste, constituting an environmentally sustainable source for the concrete industry. This study explores the integration of construction and demolition (C&D) waste-based RFAs in concrete, aiming to reduce carbon emissions and promote eco-friendly construction practices. The RFAs, obtained from aging concrete structures, contribute fines to the concrete mixture, and their viability is assessed in this research. The investigation encompasses strength and microstructural analyses of RFA-based concrete blends with varying replacement percentages (20%, 40%, 60%, 80%, and 100%). The comprehensive evaluation spans 7 and 28 days of water curing, with particularly promising outcomes observed up to a 40% replacement threshold. The optimal performance is identified at the 40% RFA replacement, justifying the applicability of RFAs as an alternative fine aggregate. Microstructural studies conducted through a secondary electron microscope (SEM) specifically for the 40% RFA replacement substantiate the particle packing effect and corroborate the experimental findings. The findings underscore the sustainable potential of incorporating RFAs in concrete formulations, offering a pathway toward environmentally conscious and resilient concrete structures.
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.
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 | 0.001 | 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".