Flexural Performance of Nanomodified Cementitious Composites Reinforced with BFP in Bonded Overlays
Bibliographic record
Abstract
There is strong demand for ductile cement-based materials in various rehabilitation systems of concrete infrastructure. This study investigated the flexural performance of nanomodified cementitious composites incorporating a novel class of basalt macrofibers, basalt-fiber pellets (BFP), in an overlay system. The cementitious composites comprised 50% fly ash or slag replacement with 6% nanosilica and 4.5% BFP. The cementitious composites [top (overlay) layer] were evaluated based on mechanical compatibility with intact and precracked concrete substrate (bottom layer) through flexural loading. In addition, three-dimensional finite-element models were established for these configurations to analyze the effect of overlay thickness and substrate compressive strength on the performance of the overlay systems. The results showed effective bonding of the composites with the substrate (without transverse delamination) and highlighted their contribution to enhancing the postcracking performance of the overlay system, in which the intact system of Specimens N-F-4.5 and N-G-4.5 had toughness 324% and 471%, respectively, higher than that of reference concrete specimens. Numerical analysis showed the influence of overlay thickness on the ductility of both systems (intact and precracked), whereas the substrate strength affected only the first-cracking strength of the intact system (approximately 25%, increasing the compressive strength of the substrate concrete from 40 to 50 MPa).
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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".