Mechanical Properties of Nano-Modified Cementitious Composites Reinforced with Single and Hybrid Fibers
Bibliographic record
Abstract
High-performance cementitious composites (HPCC) are prominently featured with high tensile ductility and toughness. Slag has been widely used in HPCC; however, HPCC with high volumes of slag has low matrix strength and limited development of micro-structure at early-age. These limitations can be mitigated by incorporating nano-particles (e.g., nano-silica) in the binder. The purpose of this study was to develop nano-modified HPCC with high ductility and matrix quality. A new form of basalt fibers termed basalt fiber pellets (BFP)—basalt fiber strands encapsulated by a polymeric resin—were used at different dosages (2.5% and 4.5% by volume), and in a hybrid system with PVA fibers (1% by volume) to develop in these composites. All composites incorporated a binder consisting of 50% general use cement and 50% slag with the addition of 6% nano-silica. The composites were tested in relation to compressive strength and flexural performance. All the nano-modified composites showed improved performance, especially at early-age, despite the high volume of slag incorporated in the binder. While the compressive strength of the mixtures was reduced with increasing the dosage of BFP, addition of 1% PVA fibers to BFP (hybrid system) enhanced the compressive strength of the composites. In the same context, the flexural performance of the composites comprising hybrid fibers was also improved in relation to flexural strength, post-cracking behavior, residual strength and toughness. Therefore, these composites have a promising potential for infrastructure applications requiring improved strength and ductility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".