Effect of different exposure conditions on the self‐healing capacity of engineered cementitious composites with crystalline admixture
Bibliographic record
Abstract
Abstract In this study, the effect of different exposure conditions of ambient air, tap water, and seawater on two levels of crack widths in engineered cementitious composites (ECC) were investigated. Crystalline admixture (CA) was implemented in the ECC mix to promote self‐healing capacity. Flexural testing on prism specimens (100 × 100 × 350 mm) was conducted to evaluate self‐healing by recovering the stiffness in the specimens with crack widths below 200 μm. Water permeability test was also carried out to assess the crack‐filling capability of ECC disk specimens (100‐mm diameter × 50‐mm thickness) with single crack widths over 1 mm. Digital image correlation technique was used to monitor crack propagation patterns and crack widths. To analyze the microstructure of the healing products, X‐ray diffraction test was conducted on groups of tap water and seawater exposures. Concluding results proved seawater to be a promising environmental condition for the self‐healing process in ECC specimens incorporating CA, in terms of recovery of mechanical and transport properties. Brucite was found to be formed as the additional healing agent that promoted self‐healing in this condition. These results can be applied for coastal concrete structures.
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".