Durability of blended cement against sodium sulphate attack and alkali-silica reaction
Bibliographic record
Abstract
Blended cements were studied for their efficacy against sulphate attack and alkali-silica reaction using six different types of fly ashes, a slag, a silica fume and four types of General Use Portland cement of different alkalinity. The study results showed that low calcium fly ash, silica fume and ground granulated blast furnace slag enhanced the sulphate resistance of cement with increased efficacy with the increase in the replacement level. However, slag and silica fume, especially at low replacement levels, exhibited increased rate of expansion beyond the age of 78 weeks. On the contrary, high calcium fly ashes showed reduced resistance to sulphate attack with no clear trend between the replacement level and expansion. Ternary blends consisting of silica fume, particulary in the amount of 5%, high calcium fly ashes and General Use (GU) cement provided high sulphate resistance, which was attributable to reduced permeability. In the same way, some of ternary blends consisting of slag, high calcium fly ash and GU cement improved sulphate resistance. Pre-blending optimum amount of gypsum with high calcium fly ash enhanced the latter's resistance to sulphate attack by producing more ettringite at the early stage of hydration. In the context of alkali-silica reaction permeability was found to be a contributing factor to the results of the accelerated mortar bar test. High-alkali, high-calcium fly ash was found to worsen the alkali silica reaction when used in concrete containing some reactive aggregates. Ternary blend of slag with high calcium fly ash was found to produce promising results in terms of counteracting alkali-silica reaction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".