Durability performance of low-carbon concrete incorporating optimized ratio of multiple waste materials (glass powder, biomass fly ash, and shredded rubber)
Bibliographic record
Abstract
Incorporating waste materials in concrete can enhance sustainability and contribute to more environmentally friendly construction practices. However, using these materials in concrete presents challenges related to durability, mechanical, and environmental performance. This study explores the effects of incorporating glass powder (GP), biomass fly ash (BFA), and shredded rubber (SR) as partial replacements for cement and aggregates in concrete. The focus is on assessing their impact on durability, mechanical properties, and the environment. To determine the most effective combination of waste materials, Response Surface Methodology (RSM) is employed to design the experimental program and optimize the mixture proportions. The research evaluates air content, freeze-thaw resistance, compressive strength, Young’s modulus of elasticity, splitting tensile strength, modulus of rupture, surface electrical resistivity, life cycle assessment (LCA), rapid chloride penetration test, and global warming potential of concrete mixtures. Results show that replacing cement with 20% GP improves durability and strength, raising electrical resistivity by 240% and achieving durability factors of more than 90%. However, SR above 7.5% reduces freeze-thaw resistance and stiffness. In addition, optimal mixes with a maximum of 16% GP or 15% BFA reach a compressive strength of 30 MPa and limit GWP to 297 kg CO₂-eq/m³.
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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".