Response of concrete with blended binders and nanoparticles to sulfuric acid attack
Bibliographic record
Abstract
Concrete has long been the most popular material for the construction of key infrastructural elements such as sewerage pipes, water treatment facilities, industrial floors and foundations, which can be chemically vulnerable to damage by sulfuric acid attack. Since high alkalinity is required for stability of the cementitious matrix, concrete is susceptible to attack by acidic media, which may disintegrate the hydrated cement paste to various levels based on the prevailing exposure conditions and key mixture design parameters of concrete. This study investigated the response, in terms of physico-mechanical and microstructural features, of concrete comprising different types of cement (general-use or Portland limestone cement (PLC)) with various combinations of supplementary cementitious materials (fly ash, silica fume and nanosilica) to severe sulfuric acid exposure (immersion of test specimens in 5% sulfuric acid solution with a maximum pH threshold of 2·0 for 90 d). The results revealed that the surface degradation (mass loss) of concrete under severe sulfuric acid attack was independent of its penetrability (physical resistance), since very dense cementitious matrices (low penetrability) suffered severe deterioration. PLC may slightly improve the resistance of concrete to sulfuric acid attack whereas, among the blended binders tested, binary binders comprising 30% fly ash particularly improved the resistance of concrete to sulfuric acid attack due to an inert filler effect of fly ash particles at the exposed surface.
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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".