Utilization of Novel Basalt Fiber Pellets from Micro- to Macro-Scale, and from Basic to Applied Fields: A Review on Recent Contributions
Bibliographic record
Abstract
Fiber-reinforced cementitious composites (FRCC) are one of the leading engineering materials in the 21st century, as they offer proficiency in enhancing strength, ductility, and durability in structural engineering applications. Because the recently developed basalt fiber pellets (BFP) offer combined strands of fibers encased in a polymer matrix, they are being prevalently studied to explore new possibilities when used in brittle materials such as mortar and concrete. Hence, this paper synthesizes the intensive research efforts and contributions to this novel class of fibers conducted by the authors. Specifically, it reviews the fresh, mechanical, and durability properties of FRCC incorporating single BFP or hybrid with polyvinyl alcohol fibers and modified with slag/fly ash and nano-materials and its suitability for different field applications. In addition, the nano- and meso-scale modeling of such matrices are described. BFP significantly contributes to improving post-cracking flexural behavior by toughening the cementitious matrix and minimizing strength losses when exposed to harsh environments. All results show promising progress in the development of high-performance FRCC comprising BFP, with potential success for structural and pavement 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".