Mechanical Properties of Hybrid Structures Incorporating Nano-Silica and Basalt Fiber Pellets
Bibliographic record
Abstract
Recently, developing a nonferrous reinforcement system (corrosion-free system) using durable and ductile cement-based materials that incorporate discrete fibers has been a promising option for exposed concrete structures in cold regions or marine environments. Therefore, in this study, properties of a novel type of cementitious composite comprising nano-silica and a high dosage of slag were investigated. The hybrid (layered) composites assessed in this study were composed of two layers of different types of cementitious composites. Normal concrete (NC) was used in the top layer combined with a layer of fiber-reinforced cementitious composite (FRCC) reinforced with either the recently developed basalt fiber (BF) pellets (basalt fiber strands encapsulated by a polymeric resin or steel fibers (SF)) that were used at different dosages. The post-cracking behavior in terms of residual strength, residual index, and toughness are presented and discussed. The analysis of results showed the effectiveness of the BF pellets in enhancing the post-cracking behavior of specimens, as they behaved comparably to counterpart specimens comprising SF, which makes them a good candidate for infrastructural applications including rehabilitation such as new bridge girders or overlays.
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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) | 0.001 | 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".