Enhancing the self-healing properties of engineered cementitious composites by the application of super-sulfated cement
Bibliographic record
Abstract
No research has been reported on the self-healing performance of engineered cementitious composites (ECCs) prepared with super-sulfated cement (SSC), despite its potential to reduce carbon dioxide emissions compared with ordinary Portland cement (OPC). This significant gap in the research literature was addressed by exploring the influence of SSC on the recoverability of pre-cracked ECC samples. In addition to mechanical characterisation of sound samples, an exhaustive investigation was undertaken to comprehensively assess the self-healing ability of pre-loaded SSC-based ECCs by means of flexural strength tests, deflection measurements, ultrasonic pulse velocity (UPV) tests and rapid chloride permeability tests. Scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS) was used to evaluate the microstructural changes and development of self-healing products within the microcracks of SSC mixtures prepared with various amounts of fly ash. The SSC-based ECCs, while maintaining comparable mechanical and ductility properties, exhibited significant improvements in recovery rates (more than 27%, 7% and 76% for flexural strength, UPV and chloride permeability, respectively, compared with the OPC-based control ECC). The SEM–EDS results confirmed the enhanced precipitation of ettringite as a new self-healing product related to the inclusion of SSC in ECCs, along with conventional calcium silicate hydrate/calcium aluminium silicate hydrate (C-S-H/C-A-S-H) gels.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".