External Sulfate Attack of Ambient-Cured One-Part Alkali-Activated Self-Consolidating Concrete
Bibliographic record
Abstract
The mechanism of sulfate attack on alkali-activated materials, particularly the alkali-activated self-consolidating concrete (AASCC), is complex and contradictory. This could be due to the wide range of precursor and activator materials used in the production of AASCC mixtures, which has called into question the reliability and validity of existing evaluation procedures and practices. This paper presents a systematic research effort on AASCC mixtures, based on granulated blast-furnace slag, prone to various sulfate attack scenarios that are thought necessary to establish a proposed criterion. The conducted experimental design demonstrated that single-, binary-, and ternary-precursor AASCC samples, activated with 1:1 Na2CO3 and MetaNa2SiO3, partially submerged in sodium, magnesium, and mixed sulfate solutions could experience a dual sulfate attack scheme. Sulfate attack can occur in the immersed section in sulfate solutions, while physical sulfate attack can occur in the portion above the solution level. The influence of physical sulfate attack on the concrete’s characteristics was not significant given that the damage was confined to the outer surface. However, the damage was primarily monitored by the AASCC different systems’ pore structure, which resulted in the leaching of ions from samples to solutions. It was found that maintaining the pH in the sulfate solutions increased the rate of damage of AASCC mixtures. Furthermore, binary, and ternary precursor blends partially replacing slag with SF, or FA resulted in decreased porosity, surface scaling, and AASCC deterioration caused by an expansion in the volume of very small diameter pores. Finally, in all AASCC systems, gypsum and ettringite were the primary degradation products of sulfate 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".