Evaluating the efficiency of SCMs to avoid or mitigate ASR-induced expansion and deterioration through a multi-level assessment
Bibliographic record
Abstract
It is widely accepted that Alkali-Silica Reaction (ASR)-induced expansion and deterioration may be prevented by the appropriate use of supplementary cementing materials (SCMs). Nevertheless, the correlation between microscopic/macroscopic damage degree and features of SCMs-made concrete is still not fully understood. Moreover, more research needs to be conducted to understand the impact of SCMs on the elastic properties of ASR-secondary products. This research presents the results of a multi-level assessment of ASR-induced damage development in concrete incorporating a wide range of reactive aggregates and SCMs at selected unrestrained expansion levels (i.e., 0.00 %, 0.05 %, 0.12 %, 0.20 % and 0.30 %). Furthermore, a thorough discussion is made on the damage generation and propagation at distinct locations of affected concrete: aggregate particles, ITZ and cement paste. Results indicate that higher volumes of SCMs can slightly change ASR damage development by lowering the formation of cracks in the cement paste and altering ASR reaction products' chemo-mechanical properties.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".