Laboratory Assessment of the Potential Rate of ASR Expansion of Field Concrete
Bibliographic record
Abstract
Abstract In-situ monitoring of concrete deformations and movements is the best way to assess the current expansion of concrete members affected by alkali-silica reactivity (ASR). However, laboratory tests on cores are less expensive and more rapid, and are commonly used to assess the potential for further expansion due to ASR. The risk of expansion and damage due to ASR can be reasonably assessed in the laboratory from: (1), the inherent expansivity of the concrete under study, which is determined by testing core samples in air at 100% RH and 38°C; (2), the residual absolute reactivity of the aggregates present in the concrete under study, which can be determined by testing core samples in 1N NaOH solution at 38°C or, even better for coarse aggregates, by testing aggregates extracted from cores through the concrete prism test CSA A23.2-14A or ASTM C 1293; (3), the amount of alkalies that are still active in the concrete, i.e. in the pore solution, which is estimated by a hot-water extraction method on ground concrete, and (4), humidity, (5), temperature, and (6), stress conditions (confinement, reinforcement, pretensioning, postensioning) in service. The individual risk indices corresponding to each of the above parameters are combined to determine the potential rate of ASR expansion of concrete members in service, either already affected by ASR or not.
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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 | 0.000 | 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".