Experimental Investigations of the Effect of Selected Admixtures on the Resistance of Concrete to Sulfuric Acid Attack
Bibliographic record
Abstract
Each year billions of dollars are spent around the world on the rehabilitation and/or replacement of concrete sewer pipes damaged by sulfuric acid corrosion. This paper reports the findings of an extensive experimental program undertaken to evaluate the resistance of 17 different concrete mix designs with different additives and w/c ratios to sulfuric acid attack. Silica fume, Metakaolin and Organic Corrosion Inhibitors (OCI) in different concentrations were added to the cement to improve the resistance of the concrete paste to sulfuric acid attack. The weight loss of the concrete samples immersed in two concentrations of sulfuric acid (3% and 7%) was measured to evaluate the degree of concrete degradation. The effect of the various admixtures on the compressive strength and porosity of the concrete samples were also evaluated in an effort to determine the relationships between these attributes and the resistance of the concrete samples to a sulfuric acid attack. Data from a two months exposure test showed that the addition of Metakaolin or OCI to the concrete mix resulted in a significant improvement in the concrete resistance to sulfuric acid attack compared to the control mix. Silica fume treated concrete exhibited little or no improvement compared to the control mix. The data also showed that the compressive strength and porosity of the concrete provide little guidance if any to the resistance of the concrete paste to sulfuric acids 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".