Assessing the self-healing capability of cementitious composites under increasing sustained loading
Bibliographic record
Abstract
This study investigated the effects of progressively increasing sustained loading on self-healing behaviour of 180-d-old microcracked engineered cementitious composites (ECCs) incorporating different mineral admixtures. After introducing microcracks to the specimens with applied severe pre-loading, some were subjected to progressively increasing sustained loading. All of the specimens were then subjected to continuous moist curing for 150 d to evaluate self-healing performance. Mechanical property (modulus of rupture (MOR) and mid-span beam deflection) characterisations and ultrasonic pulse velocity measurements were used to assess self-healing capability. Experimental results showed that even under progressively increasing sustained mechanical loading, MOR results greater than the original values could be obtained, depending on mineral admixture selection. Although deflection results were more adversely affected by progressive sustained loading compared to MOR results, even the lowest deflection value obtained from different ECCs was still more than 100 times that of conventional concrete after healing. Under continuous moist curing, there were minimal changes in ultrasonic pulse velocity results of all ECCs subjected to progressively increasing sustained loading, so that recovery results similar to those of specimens without sustained loading were obtained, despite the fact that ultrasonic pulse velocity testing was not that sensitive in capturing the effects of self-healing.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".