Alkali–aggregate reaction: performance testing, exposure sites and regulations
Bibliographic record
Abstract
Several laboratory performance tests for alkali–aggregate reaction (AAR) have been used worldwide for about 20 years. However, a review performed one of the present authors in 2006 concluded that none of the test methods meets all the criteria for an ideal performance test. In the last decade, research has been performed in several countries with the aim to improve current AAR performance test methods and develop alternative tests. The Rilem (The International Union of Laboratories and Experts in Construction Materials, Systems and Structures) Technical Committee (TC) 258-AAA (2014–2019) is also focusing on this topic. The main purpose of this technical committee is to develop and promote a reliable performance-based testing concept for the prevention of deleterious AAR. Strong emphasis will be placed on the implementation of the Rilem methods and recommendations as national and international standards. It is crucial to link the results from the accelerated laboratory testing to field behaviour, primarily against field exposure sites. Some results from such exposure sites are included in this paper.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".