Engineered Cementitious Composites with Super-Sulfated Cement: Mechanical, Physical, and Durability Performance
Bibliographic record
Abstract
This study aimed to bridge a research gap by exploring the utilization of super-sulphated cement (SSC) in engineered cementitious composites (ECCs) as a sustainable alternative to ordinary Portland cement (OPC)-based mixtures. The SSC was designed with slag, gypsum, and a small amount of OPC. The primary objective was to investigate the effects of incorporating SSC, both with and without fly ash (FA), at various FA/SSC ratios between 0 and 1.5. A comprehensive evaluation was conducted to assess the performance of the ECC-SSC mixtures, including the compressive and flexural strengths, ductility, ultrasonic pulse velocity, rapid chloride permeability, and drying shrinkage. Additionally, advanced microstructural evaluation techniques such as scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) analysis as well as X-ray diffraction (XRD) analysis were employed to analyze the reaction products in selected mixtures. The results showed that the ECC mixture produced with SSC exhibited comparable strength to the ECC-OPC. In general, all the SSC-based ECCs fulfilled the criteria for various engineering applications, especially when the fly ash to SSC ratios were 0 and 0.8. In addition, ECCs with FA/SSC ratios of 1.2 and 1.5 showed ultra-ductile performance higher than the control ECC. Interestingly, all the FA-based ECC-SSC presented lower shrinkage characteristics than the control OPC-based ECC.
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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".