Effect of Self-Healing on the Different Transport Properties of Cementitious Composites
Bibliographic record
Abstract
This research focuses on the effects of self-healing on the different transport properties of microcracked Engineered Cementitious Composites (ECC) with different maturity levels and incorporating three different mineral admixtures with greatly varying chemical compositions. The effect of self-healing capability on transport properties was assessed using water sorptivity and rapid chloride permeability tests (RCPT). Experimental results revealed that with the selection of proper mineral admixture type and conditioning, a 92% recovery in water sorptivity results is attainable. Moreover, a considerable amount of this recovery took place after only 7 days of water curing, significantly lowering the risk of water transport by capillary suction into cracked ECC. Like the sorptivity measurements, most of the chloride ion penetrability values could also be reduced up to a great extent after 30 days of water curing, so most of the results fell into the low penetrability level during this period, as prescribed by ASTM C1202. Although self-healing in terms of RCPT results started to be visible in the first 7 days of water curing, significant improvements needed more time in RCPTs, unlike the sorptivity results. Overall, these findings suggest that the rate of self-healing varies depending on the different transport mechanisms dominant in a given infrastructure type during its service life.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".