Effects of supplementary cementitious materials on the durability of glass aggregate mortars
Bibliographic record
Abstract
The durability performance and sustainability of concrete and mortar can be enhanced by incorporating supplementary cementitious materials (SCMs), such as fly ash, slag (SG), silica fume (SF), and metakaolin (MK). In this study, binary and ternary blends of the aforementioned SCMs were investigated to determine an optimal combination that enhances the durability of glass aggregate mortars. Compressive strength, chloride permeability, and sorptivity experiments were conducted for all mixtures at specimen ages of 28 and 90 days. Fourteen day alkali silica reaction tests were also performed to determine the expansion properties. It was determined that ternary blends of fly ash and SF effectively mitigate expansions, with mixtures containing fly ash being most effective due to its high silica (SiO2) content. Ternary blends of SG and SF were also found to be successful in reducing permeability. In addition, it was determined that optimal durability performance can be achieved with 10% MK and SF replacements.
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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".