Effect of fineness of high-alumina ground granulated blastfurnace slag on magnesium sulphate attack
Bibliographic record
Abstract
This paper describes the sulphate resistance of hardened mortar and paste specimens incorporating 50% ground granulated blastfurnace slag (BFS). The main variable is the fineness level of the slag materials, which are divided into 445, 600 and 800 m2/kg Blaine surface area. Ordinary Portland cement mortar and paste were also used to compare the degree of deterioration by sulphate attack. All mortar and paste specimens were exposed to 4·24% magnesium sulphate solution at 20 ± 1°C for 540 days. The tests used in this study include visual examination, compressive strength and mass loss for mortar samples, and microstructural observations such as X-ray diffraction (XRD), differential thermogravimetric analysis and scanning electromicroscopy for paste samples. Experimental results indicated that mortar specimens with a high-fineness ground granulated blast-furnace slag at 50% replacement showed a poor resistance to magnesium sulphate attack compared with those of lower fineness. The XRD results confirmed that gypsum formation was primarily responsible for the deterioration in the hardened cement matrices of mixtures with high-fineness ground granulated blastfurnace slag. In addition, the conversion of cementitious C–S–H to M–S–H (or M–C–S–H) also led to the severe deterioration in the mortar specimens with a 800 m2/kg fineness level. This work suggests that care should be taken when using BFS with high-fineness level under magnesium sulphate attack.
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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".