Mechanical performance of engineered cementitious composite incorporating glass as aggregates
Bibliographic record
Abstract
Engineered cementitious composites (ECC) are gaining massive attention in the construction industry due to their enhanced mechanical and durability properties compared to that of conventional concrete. However, the high cost associated with ECC as a result of the use of ultrafine silica sand has limited its widespread applications. Therefore, this study was carefully designed and carried out to develop a cheaper and eco-friendly ECC by incorporating glass in the form of beads as aggregates in ECC. This study employs the use of glass to replace the ultrafine silica sand in the ECC in the range of 0–100%. The mechanical performance of the ECC mixtures in terms of the compressive, flexural and tensile properties was evaluated. Results from this study showed that glass can serve as an eco-friendly alternative to the ultrafine silica sand up to 100% replacement in ECC mixtures without any detrimental effects on the mechanical properties. The use of only glass as aggregate in ECC mixtures resulted in a 5.3%, 21.5% and 32.5% increase in the compressive, tensile and flexural strengths, respectively. Sustainability and cost analysis of the mixtures showed that the use of glass as aggregate in ECC mixtures can be used to reduce the cost and embodied carbon by 16.6% and 5.9%, respectively. Also, ECC mixture with only glass as aggregate exhibited strain-hardening like behaviour with multiple cracks formation. Microstructural investigation showed that fibres are well distributed in the matrix.
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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) | 0.002 | 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".