Development of a durability indicator to forecast the efficiency of preventive measures against external sulphate attack
Bibliographic record
Abstract
Currently, the C3A content of binders is considered the most important factor contributing to external sulphate attack (ESA) deterioration. However, portlandite is also deemed to play a major role in ESA development. Yet, there are very few researches on this topic. This paper evaluates physical (i.e., induced expansion and mass variation, ultrasonic pulse velocity, dynamic modulus of elasticity, modulus of rupture and compressive strength) and chemical (i.e. X-ray diffraction and thermogravimetry) properties of seven mortar mixtures presenting distinct binders (i.e. cement types, inert fillers and supplementary cementing materials) and exposed to different sulphate solutions (i.e. sodium and magnesium). Correlations are conducted between data obtained in the laboratory, and a theoretical approach to describe cementitious mixtures' susceptibility against ESA is then proposed. Results show that the proposed durability indicator (i.e., predicted portlandite amount and potential of ettringite formation) are well correlated with ESA-induced expansion and damage. Moreover, the influence of portlandite on ESA seems to depend on the type of sulphate attack (i.e., Na2SO4 or MgSO4). Finally, highly reactive SCMs and consequent higher portlandite consumptions seem to increase the overall deterioration due to MgSO4 exposure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".