Tomography-Based Investigation on the Carbonation Behavior through the Surface-Opening Cracks of Sliced Paste Specimen
Bibliographic record
Abstract
Understanding the cracking behavior during carbonation is of high importance, and the cracks can serve as a shortcut for CO2 diffusion, which can further accelerate the carbonation process itself. In this study, a sliced paste sample was taken for an accelerated carbonation test, and the cracking behavior, as well as its impact on carbonation, was investigated through a novel extended attenuation method based on X-ray (XRAM) which is performed primarily on computed tomography (CT). Surface-opening cracks at different carbonation ages were rendered, based on which a full view on the carbonation-cracking behavior was built. The results reveal that the crack paths can rapidly be occupied by CO2, and that leads to the generation of V-shaped carbonation cusps pervading the carbonation fronts. The V-shaped carbonation cusps were mostly generated at the early carbonation age (within 14 days), attesting to a less intact sample surface as compared to the inside area. Moreover, this study confirms that the carbonated area would split into two independent zones with variant carbonation degree due to the increased humidity level near the sample surface. The current work reveals the interconnection between carbonation and cracking, and the results can be used for the designing of cement-based materials with better carbonation and cracking resistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".