Assessment of the efficiency of distinct surface treatments to mitigate ASR-induced development
Bibliographic record
Abstract
Over the years, various coating materials have been used to mitigate/rehabilitate concrete once alkali-silica reaction (ASR) takes place. Although promising results are demonstrated, their efficiency is compromised by the progression of ASR and crack formation. Recently, new coatings with higher penetrability and enhanced self-healing properties have shown good performance against distinct durability problems in concrete, yet their behaviour under ASR development is unknown. This research appraises the ability of hydrophilic self-healing coating (CA) mixtures to mitigate concrete deterioration caused by ASR in its initial, moderate and advanced phases. Their efficiency is multi-level assessed, and comparisons with other systems (e.g., silane/siloxane, rigid-coating and lithium-based) are also performed. Results indicate that the surface treatments with CA changed ASR kinetics while not altering the ASR mechanism of deterioration. Finally, qualitative charts are provided to help select different types of surface treatments and the most appropriate “timing” for their application. • Coating and sealers treatments modified ASR kinetics. • Although changing ASR kinetics, the surface treatments did not change the deterioration mechanism. • Coating with crystalline waterproofing and water repellent showed higher efficiency in lowering ASR. • Coatings containing enhanced self-healing properties sealed cracks with a maximum 7 mm depth. • The qualitative charts can help decide the most appropriate time to apply different treatments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".