Mitigation of Temperature-Induced Curling of Concrete Roadbed along High-Speed Railway: In situ Experiment and Numerical Simulation
Bibliographic record
Abstract
The concrete roadbed in the Beijing-Shenyang high-speed railway (HSR) is being serviced for the first time in HSR construction history. Rail inspections have shown that the extreme temperature conditions in seasonally freezing regions can significantly influence the curling behavior of concrete roadbeds. This paper presents an in situ experiment to fundamentally evaluate the impact of seasonal temperature variations on the curling behavior of concrete roadbeds. Herein, a thermomechanical coupled finite element (FE) model is built and calibrated with experimental data. Then, specific consideration is given to the curling mitigation measures, including adjusting the thickness, length, and construction form of the concrete roadbed. Mitigating upward-curling behavior by increasing the thickness of the concrete roadbed will result in severe downward-curling behavior during one year of service. Finally, an active groove-setting construction form is suggested to prevent curling from the temperature variations in the concrete roadbed. In general, this study further enhances the common understanding of the temperature curling behavior of concrete roadbeds serviced in an HSR.
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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 | 0.001 |
| 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 | 0.001 | 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".