Self‐healing of engineered cementitious composites under reversed and sustained loading conditions
Bibliographic record
Abstract
Abstract The present study investigates the effects of reversed and sustained flexural loading cycles on the repeatability of self‐healing in engineered cementitious composites (ECC). The experimental work is designed to test three cases of normal (REF), reverse (REV), and reverse‐sustained (RES) loading and three different exposure conditions of tap water, sea water, and open air. A total of 27 prism specimens (100 × 100 × 350 mm) were fabricated, and a four‐point bending test was used for flexural load application at different stages and to further measure the recovery in mechanical properties. The research is proposed to elaborate on the positive/negative impact of compression cycle on the self‐healing of cracks. Ultrasonic pulse velocity (UPV) test was carried out before and after each loading and the wave travel time was compared as an indicator of healing efficiency. To monitor crack propagation patterns and crack widths, digital image correlation (DIC) technique was used. To further analyze the mineralogy and microstructure of the healing products, X‐ray diffraction (XRD) test was conducted on two groups of tap water and sea water exposures. Concluding results proved that sea water and tap water are suitable environments for autogenous self‐healing process. Furthermore, reverse loading cycles were demonstrated to impact the self‐healing results and should be considered for repeatable self‐healing evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".