Effect of autogenous crack self-healing on mechanical strength recovery of cement mortar under various environmental exposure
Bibliographic record
Abstract
While research on self-healing of cement-based materials has recently gained considerable attention and made sizable progress, there is still ongoing debate and controversy regarding the effect of crack closing induced by autogenous self-healing on mechanical strength recovery. Despite that several techniques have been used to capture and quantify the self-healing of surface cracks, the resulting effect on mechanical strength has not, to date, been explored and quantified in a rigorous and systematic manner. Therefore, in this study, a broad array of multi-scale techniques including non-destructive shear wave velocity, high-resolution X-ray computed tomography (µCT), and 3D image analysis was deployed to examine the effects of autogenous crack self-healing on the mechanical strength recovery in various mortar specimens. The influence of microstructural changes induced by additives such as swelling compounds, silica-based additions, and carbonating minerals on strength recovery under diverse environmental exposures was further explored. The results capture the relationship between the crack closing mechanism imparted by self-healing and mechanical strength recovery, therefore elucidating the discrepancies in mechanical strength recovery results reported in the open literature.
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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 | 0.000 | 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 | 0.001 |
| 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".