Preparation and Hydration Mechanism of Low Shrinkage Railway Sleeper Concrete Containing Hot Steaming Steel Slag
Bibliographic record
Abstract
The cascade grinding technology was used to product cementitious materials with hot steaming steel slag (SS), iron ore tailings (IOT), granulated blast furnace slag (GBFS), cement clinker (CC) and flue gas desulfurization gypsum (FGDG).The effect of SS on the mechanical properties and autogenous shrinkage of railway sleeper concrete and the mechanism of SS inhibiting the autogenous shrinkage of railway sleeper concrete were studied by means of X-ray diffraction (XRD), flourier transform-infrared spectroscopy (FT-IR), and scanning electron microscope (SEM).The results show that the compressive strength of IOT railway sleeper concrete mixed with SS (SIRSC) is lower than that of IOT railway sleeper concrete unmixed with SS (UIRSC) at the same age.However autogenous shrinkage of SIRSC is significantly lower than that of UIRSC, autogenous shrinkage value of SIRSC at 28 d was 230×10 -6 , while that of UIRSC was 593×10 -6 .The hydration mechanism analysis shows that the hydration expansion of a small amount of inert f-CaO and f-MgO phase contained in the SS is the main expansion source to restrain the autogenous shrinkage of SIRSC.The f-CaO and f-MgO phases are highly dispersed after superfine grinding SS is mixed with concrete.In the middle and later stage of concrete hardening, the synergistic growth of ettringite (AFt) and C-S-H gels inhibits the autogenous shrinkage of SIRSC and improves the mechanical properties.
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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".