Study on Effect of Curing Regimes on Ultra-High- Performance Concrete by Nuclear Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
As one of the key factors influencing the hydration process, as well as the microstructure formation and evolution of ultra-highperformance concrete (UHPC), the action mechanism of different curing regimes have been studied to some extent.However, the current knowledge of the underlying mechanisms that control the different effects of different curing regimes is limited.In this study, the composition of hydration products, micromorphology, and migration and evolution of aluminum-phase hydration products of UHPC under three combined curing regimes (standard curing, steam curing + standard curing, and autoclave curing + standard curing) were investigated in depth.Micromorphology observation shows that heat treatment promoted the formation of higher-stiffness hydration products (tobermorite and xonotlite) in UHPC, and the higher the polymerization degree, the higher the Si/Ca ratio of the hydration product.Meanwhile, 29 Si and 27 Al nuclear magnetic resonance (NMR) spectroscopy shows that specimens with higher strength had higher Al[4]/Si and a lower amount of ettringite and AFm at the early curing stage.The elevated curing temperature reduced the formation of ettringite and AFm and allowed more Al 3+ to replace Si 4+ into the structure and interlayer of the calcium-(alumino)silicate-hydrate (C-(A)-S-H) gel, which increased the mean chain length (MCL) and polymerization degree of the C-(A)-S-H gel.However, the polymerization effect of Al ions is limited, so the provision of the silicon source to improve the Si/Ca ratio of the system is important.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".