The case for air-entrainment in high-performance concrete
Bibliographic record
Abstract
Freezing–thawing cycles are a major cause of degradation of concrete structures in cold climates. The use of air-entraining admixtures to induce an adequate network of closely spaced air bubbles in concrete has been proven to dramatically decrease its susceptibility to frost action. However, with the advent of high-strength high-performance concrete (HPC), the need for air-entrainment in such concrete has become a controversial issue since the 1980s. Some researchers advocate using air-entrainment in HPC, while others argue that this practice is unnecessary owing to the very low porosity and amount of freezable water in HPC. In this paper, an extensive review of the available literature indicates that there is substantial scatter and irreproducibility of data regarding this issue. There is also evidence of vulnerability of HPC to both micro and macrocracking in service and a demonstrated reduction in performance under combined ageing mechanisms. This could make non-air-entrained HPC vulnerable to freezing–thawing degradation even though such vulnerability may not be detected using classical frost durability tests. Moreover, there are practical limitations to the use of very low water/cement ratio concrete in field conditions, which make the production of HPC with negligible freezable water content difficult to achieve in full-scale structures. This paper presents evidence that supports the argument that the use of air-entrainment in HPC is a practice that provides enhanced safety, reliability and serviceability, and is therefore recommended for concrete practitioners.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".