Report of RILEM TC 301-ASR: relation between pore solution composition and ASR expansion
Bibliographic record
Abstract
Abstract An important parameter determining the risk of alkali-silica reaction (ASR) in concrete is the availability of alkali ions as they are closely related to the OH− concentration in the pore solution. Comparison of concrete or mortar expansion with cement and pore solution composition from literature data showed no obvious relationship of the expansion with the total alkali content of the cement. However, the observed expansion was strongly dependent on the alkali and hydroxide concentrations in the pore solution. At a hydroxide concentration greater than 250 mmol/L in the pore solution or at (Na + K) concentrations greater than 300–400 mmol/L, significant ASR expansion took place for the highly reactive aggregates studied in the laboratory samples exposed at near ambient temperatures (20–40 °C). Less reactive aggregates will have a higher “alkali threshold”. At 60 and 80 °C correlations between pore solution and expansion tend to fall apart as temperature influences many factors that can accelerate or slow down ASR. High Al concentration in the pore solution as well as a low undersaturation with respect to silica have been suggested to slow down ASR formation. Neither the Al concentration in the pore solution nor undersaturation with respect to SiO2 was found to be selective criteria for ASR expansion. The pH measurements are highly dependent on temperature, difficult to measure, and determined using a variety of techniques that are frequently poorly explained. The pore solution’s pH by itself is therefore not a good way to evaluate the risk of ASR expansion, although this study showed that there is a good correlation between pH and the sum of (Na + K) concentration in the paste pore solution. It is recommended that (Na + K) is used as the most reliable and relatively easily accessible parameter to indicate the potential for ASR in concretes or mortars up to 40 °C.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".