Preventing Alkali‐Silica Reaction in Concrete
Bibliographic record
Abstract
Abstract Alkali‐silica reaction continues to be a challenging durability issue for portland cement‐based concrete. While myriad of preventive options is known to reduce the risk of ASR, changes in availability and consistency of materials make either prescriptive or performance‐based approaches difficult to develop and then quickly adapt. In general, the research community has supported industry with practical solutions based on empirically derived relationships, mostly from accelerated test methods and to a lesser extent realistic exposure/field structures. It is time to increase the level of science behind our approach. The research team represented in this talk is investigating a new methodology that combines the alkali availability needed to initiate ASR (aggregate specific) with the available alkali from the total cementitious blend. The relationship between reactivity of a supplementary cementitious material and the ASR expansion is also explored. This keynote lecture will: 1) Explore performance‐based testing versus prescriptive approaches and why a hybrid approach should be considered ASR prevention; 2) Evaluate the relationship between accelerated laboratory tests, outdoor exposure blocks and field structures; 3) Examine the use of “non‐traditional” supplementary cementitious materials and/or chemical admixtures to prevent alkali‐silica reaction; 4) Propose future research needs and; 5) Make recommendations for how best to prevent alkali‐silica reactivity following the proposed approach.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".