Influence of Pore Solution and Cement Alkalinity on ASR-Expansion Behavior
Bibliographic record
Abstract
Alkali-silica reaction (ASR)in concrete has been studied extensively after it was first recognized by Stanton in the late 1930s as a source of deterioration. This article presents the influence of pore solution alkalinity and cement alkalinity(Na2Oeq %) in the concrete mix on the expansive nature of ASR expansion. MCPT - a new test method of assessing aggregate reactivity in 56-days was used throughout the experiment. This MCPT method has excellent correlation with the CPT 2-year expansion results. The Spratt limestone, a well-known reactive aggregate from Ontario, Canada was used in the experiment. The specimens were kept in a soak solution matching the predicted pore solution. The aim of the research was to find out the influence of pore solution alkalinity and cement alkalinity on the ASR expansion.It was also aimed to see if the correlation between pore solution alkalinity (also cement alkalinity) and ASR expansion is linear or not. Good linear correlation was established forpore solution alkalinity and ASR expansion at early stages (within one month) and similarly for cement alkalinity (Na2Oeq%) andASR expansion. However, the later age expansions showed that ASR expansion is more influenced by high alkaline environment than low alkaline environment and they are not proportional. This also confirms the fact that ASR distress becomes critical at high alkaline environment in the early age of the concrete structure.
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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 | 0.001 |
| 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".