Properties of High-Volume Fly Ash and Slag Cementitious Composites Incorporating Nanosilica and Basalt Fiber Pellets
Bibliographic record
Abstract
Abstract Developing ductile and durable cement-based materials that incorporate fibers has been a subject of extensive investigation to improve the performance of new and existing concrete infrastructure, especially with the advent of novel materials. In this study, properties of a novel type of cementitious composite consisting of nanosilica and a high volume of slag or fly ash were investigated. These composites were reinforced with innovative basalt fiber pellets (BFPs), which consist of basalt fiber strands encapsulated by a polymeric resin, with a specially designed grooved texture. Six mortar mixtures were designed based on cement with high-volume (50 %) slag or fly ash and 6 % nanosilica. The mixtures incorporated different dosages of BFPs (2.5, 4.5, and 6.9 % by volume). Fresh mechanical and durability tests were conducted to evaluate the behavior of the composites. In addition, thermal and microscopy studies were performed to examine the microstructural evolution of the mixtures. The results showed that the slag-based composites exhibited improved performance, especially at early ages, with a compressive strength of 57–78 MPa and toughness of 30–44 J at 28 d. Comparatively, the fly ash–based mixtures had a compressive strength and toughness of 46–53 MPa and 27–39 J, respectively at 28 d. As the dosage of pellets increased, the compressive strength was reduced, whereas the flexural behavior was significantly enhanced in terms of strain-hardening, toughness, and postcracking control. In addition, all mixtures had reduced penetrability with total passing charges less than 700 C and acceptable resistance to salt-frost scaling. Hence, all the developed composites herein had balanced plastic, mechanical, and durability performance, which makes them a viable option for a suite of infrastructural applications.
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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 | 0.001 | 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".