Balancing sustainability and strength in concrete: the combined effects of recycled aggregates, silica fume, and nylon fibers
Bibliographic record
Abstract
Concrete, widely used in construction, is a major source of greenhouse gas emissions and resource depletion due to its reliance on cement and NCA. This study evaluates the combined effects of fully replacing NCA with RCA, partially substituting cement with SF, and incorporating NWF on concrete’s engineering performance, durability, and environmental footprint. A three-way ANOVA confirmed highly significant main effects of RCA, SF, and NWF on CS ( p < 10 − 9 ) and STS ( p < 10 − 8 ); 100% RCA reduces mean CS from 76 to 61 MPa, while the synergistic addition of 10% SF and 0.25% NWF recovers 9 MPa of strength. Durability tests reveal that 100% RCA increases water absorption by 25%, but that SF–NWF combinations reduce this penalty by up to 0.48%. CIP doubles under RCA (13–26 mm) yet decreases by 7 mm with SF–NWF synergy, and sulfuric-acid mass loss rises by 3% with RCA but is lowered by 0.6% when additives are applied. A cradle-to-gate life cycle assessment using SimaPro 9.5.0.2 with Ecoinvent 3.10 and the TRACI method shows RCA alone reduces ODP by 18%, GWP by 6.6%, ACP by 8%, and EP by 10%; coupling with 10% SF and 0.1% NWF amplifies these reductions to 25%, 15.7%, 13%, and 16.5%, respectively. Heatmap and sensitivity analyses consistently identify 10SF-100RCA, 10SF0.1N100RCA, and 10SF0.25N100RCA as optimal mixes for balanced performance and sustainability.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".