Using concrete admixtures for sulphuric acid resistance
Bibliographic record
Abstract
The degradation of concrete sewer pipes by sulphuric acid attack is a problem of global scope, resulting in substantial economic losses each year. In this study, five admixtures, which offer a range of potential improvement mechanisms, were used at various dosages to enhance the resistance of concrete made with Type 50E cement to chemical sulphuric acid attack. The resistance to sulphuric acid of concrete specimens incorporating these admixtures was measured and compared to that of control specimens. An attempt was made to determine whether there is a relationship between the effect of the various admixtures on mechanical strength and porosity and the resistance of concrete to H2SO4 attack. Results indicate that metakaolin reduced the mass loss of concrete specimens due to immersion for eight weeks in H2SO4 solutions having concentrations of 7% and 3% (by volume) by 38 and 25%, respectively, compared with that of the control specimens. Other admixtures (OCI, Caltite and Xypex) reduced the mass loss of concrete specimens in the range 12–20% for the 7% H2SO4 solution and in the range 9–16% for the 3% H2SO4 solution. Although silica fume effectively increased compressive strength and reduced the porosity of concrete, its contribution to the resistance of concrete to chemical sulphuric acid was minor. No clear relationship could be established between the mechanical and physical properties of concrete (compressive strength and porosity) and its resistance to sulphuric acid attack. It was also found that the decline in compressive strength of concrete specimens subjected to H2SO4 attack was directly proportional to their mass loss, following a linear relationship.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".