AAR-Reactive Fillers in Concrete: Current Understanding and Knowledge Gaps
Bibliographic record
Abstract
The depletion of natural resources and the increasing interest in reducing CO2 emissions have heightened the demand for alternative materials in concrete production. A viable approach is to lower the clinker-to-cementitious materials ratio by partially replacing clinker with supplementary cementitious materials (SCMs) and/or alternative materials such as aggregate mineral fillers (AMFs). As the availability of SCMs is expected to decline, AMFs have been increasingly explored, including those derived from aggregate processing and susceptible to alkali-aggregate reaction (AAR). However, the behaviour of AAR-reactive AMFs in concrete remains poorly understood. This paper summarizes the current state of the art and identifies knowledge gaps concerning the use of AAR-reactive AMFs, focusing on the roles of mineralogy, particle size, replacement content, and the test methods used to assess AAR-induced development and associated microscopic and mechanical deterioration. A consistent terminology is also proposed to support future research. Finally, a theoretical foundation to understand the role of AAR-reactive AMFs in mortar and concrete is provided, and the key knowledge gaps are discussed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".