Mechanical, Physical, and Self-Healing Behaviors of Engineered Cementitious Composites with Glass Powder
Bibliographic record
Abstract
This paper presents a detailed study on the use of glass powder (GP) as a binder in engineered cementitious composites (ECC). It investigates the effect of different levels of GP on the mechanical, physical, and self-healing efficiency of ECC. To assess recovery in GP-ECCs, multiple beams were preloaded up to 60% of their original flexure deformations at the age of 28 days and left to heal under moist curing. Compressive and flexural strengths, midspan beam deflection capacity, rapid chloride penetration, and resistivity tests were used to assess the performance of different ECC mixtures. To better understand the effect of GP content on the self-healing quality of ECCs, microstructural analysis was also performed via scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) and X-ray diffraction (XRD) in the surface and core regions of healed cracks. The results of this study show that production of ECCs with GP is possible, even at 100% GP replacement level with fly ash (FA). Acceptable physicomechanical behaviors can be achieved with 50, 75, and 100% GP replacement, with better performance at 25%. This study also confirms the good self-healing capability of GP-ECCs, especially at a 25% replacement level.
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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".