Performance evaluation of fresh-to-fresh cast SCC/ECC and SCC/UHPC composites under the coupling effect of freeze-thaw and deicing solution
Bibliographic record
Abstract
This study addresses the research gap in freeze-thaw performance of fresh-to-fresh cast (hot-jointed) composites by examining the effects of freeze-thaw cycles and sodium chloride on self-consolidating concrete (SCC) combined with engineered cementitious composites (ECC) or ultrahigh performance concrete (UHPC). The potential use of hot-joint techniques for integrating ECC and UHPC with SCC to promote the sustainable application of these modern and efficient concretes is sought, while addressing the mechanical and durability limitations of SCC when exposed to freezing temperatures. The impact of fiber reinforcements in tensile ECC and UHPC layers has also been evaluated by comparing polyvinyl alcohol (PVA) and steel fibers. After 150 and 300 saline freeze-thaw cycles, flexural, compressive, and tensile bond strengths were tested. Microstructural degradation was analyzed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) was employed to detect chloride-bound phases. Results showed that composite systems had superior freeze-thaw resistance compared to mono-SCC. Chloride penetration was observed throughout SCC layers but was significantly reduced at bond layers with ECC and UHPC, especially in the UHPC based composites. PVA were more effective than steel fibers in reducing chloride binding and lowering corrosion risk.
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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".