Effect of Rubber Aggregate and Binary Mineral Admixtures on Long-Term Properties of Structural Engineered Cementitious Composites
Bibliographic record
Abstract
This paper investigates the effect of replacing silica sand (SS) with waste rubber aggregates on the long-term mechanical and transport properties of structurally engineered cementitious composites (ECCs). ECC mixtures prepared with 0 to 30% crumb rubber sand (CRS) or 30% to 40% powder rubber sand (PRS) were tested for up to 360 days of curing. The addition of ground granulated blast-furnace slag (GGBS), metakaolin (MK), and silica fume (SF) in PRS-ECCs with incorporated binary mineral admixture–based fly ash (FA) was also investigated. The compressive and flexural strengths, midspan beam deflection capacity, chloride permeability, water sorptivity, and electrical resistivity of 17 ECC mixtures were studied at different ages. The diverse aggregate vicinities and their interfacial transition zone aspects were analyzed at 28 and 360 days using scanning electron microscopy and energy-dispersive X-ray spectroscopy. The results indicate that, from 28 to 360 days of curing, ECC mixtures with CRS and PRS exhibited lower mechanical strengths and enhanced deflection capacities compared to the control mixture. However, ECCs with up to 40% PRS content showed better transport properties than the control ECC mixture, especially when incorporating a binary mineral admixture, including MK and FA. Microstructural investigations confirmed the presence of some gaps at the rubber aggregate interface, which healed partially or completely with long curing times.
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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".