Effects of nano-SiO2 coating and induced corrosion of steel fiber on the interfacial bond and tensile properties of ultra-high-performance concrete (UHPC)
Bibliographic record
Abstract
In this study, the effects of nano-silica (SiO2) coating and induced corrosion of steel fibers on the interfacial bond and tensile properties of ultra-high-performance concrete (UHPC) were investigated. Two different types of steel fibers were prepared: plain and nano-SiO2-coated. Corrosion was induced in each of these to two different degrees (2% and 5% by weight) using a 3.5% standard sodium chloride (NaCl) solution. The test results indicate that nano-SiO2 coating increases the bond strengths of steel fibers embedded in UHPC by approximately 50%. Furthermore, more scratches and higher hydrate contents were detected on the surface of nano-SiO2 coated steel fibers after pulling out from UHPC. Under tension, the UHPC containing nano-SiO2-coated steel fibers exhibited double the strain energy density, compared to that containing plain steel fibers. Moderately corroded steel fibers resulted in higher interfacial bond strength and energy absorption capacity owing to the increased surface roughness. In addition, the nano-SiO2 coating enhanced the tensile performance of UHPC even under corrosive environments. This enhancement was however diminished by steel fiber corrosion, so that fiber corrosion needs to be carefully controlled when nano-SiO2-coated steel fibers are used for 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) | 0.000 | 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".