Substitutive effect of nano-SiO2 for silica fume in ultra-high-performance concrete on fiber pull-out behavior
Bibliographic record
Abstract
This study investigated the effect of substituting nano-SiO 2 for silica fume on the fiber-matrix interfacial bond performance of ultra-high-performance concrete (UHPC). In this study, silica fume was substituted by nano-SiO 2 in the weight range of 0–50%. The degree of pozzolanic reaction of binder materials was evaluated using the thermogravimetric analysis (TGA) and compressive strength measurement. The single fiber pull-out test was conducted along with a measurement of autogenous shrinkage to evaluate the interfacial bond. The degree of pozzolanic reaction of nano-SiO 2 was found to be higher than that of other binder materials. Although the packing density was predicted to increase continuously up to a substitution ratio of 50%, the highest compressive strength was obtained when 10% of silica fume was replaced by nano-SiO 2 , which improved the compressive strength by 5.9% compared to that of the plain sample. The autogenous shrinkage increased with an increasing content of nano-SiO 2 up to 30%; however, it remained similar beyond the nano-SiO 2 content of 30%. The best pull-out performance was obtained when 20% of silica fume was replaced by nano-SiO 2 , in which the average bond strength and pull-out energy were improved by approximately 21 and 68%, respectively. Therefore, substitution of 10–20% of silica fume by nano-SiO 2 was recommended as an optimal amount considering the improvements of the compressive strength and fiber-matrix bond performance of UHPC.
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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) | 0.000 | 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".