Influence of engineered self-healing systems on ASR damage development in concrete
Bibliographic record
Abstract
Supplementary cementing materials (SCMs) have proven effective in minimizing alkali-silica reaction (ASR) development. In addition, crystalline admixtures (CAs) have been identified as potential solutions to counteract damage in concrete. However, limited data on this topic is available in the literature. This study investigates the impact of CA on concrete damage and is divided into two phases: 1) the effectiveness of CA in self-healing cracks and restoring the mechanical properties of mechanically damaged concrete; 2) it explores concrete mixtures incorporating a wide range of binder compositions (i.e., general use type cement, silica fume, fly ash, slag and Metakaolin) and chemical admixtures (i.e., commercially available CAs and modified versions) in conditions enabling ASR development. Both phases involve microscopic/mechanical analyses to assess the effects of CA on damage, and comparisons with concrete mixtures without CAs are made. The results reveal that CA enhanced the self-healing of cracks up to 82 % of cracks in cement paste (115 % higher values than concrete mixtures without CA) and restored 69 % of compressive strength. Furthermore, although CAs could change the damage mechanism of ASR, they did not “safely” mitigate it. However, combining SCMs and CAs effectively reduces ASR-induced expansion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".