NUMERICAL METHODS OF HYDRO-DYNAMIC COUPLING IN SATURATED SOIL AND ITS APPLICATION TO RAILWAY ENGINEERING
Bibliographic record
Abstract
Recent field case study shows that the roadbed of ballastless high-speed railway experienced water-induced defect such as excessive fines pumping and even local subgrade-track contact loss affecting the normal operation of highspeed train due to water immersion through gaps of waterproof materials in expansion joints between the concrete base, particularly in rainy seasons. However, the study about the dynamic behavior of high-speed railway subgrade involving water is currently rare. Based on the theory of fluid dynamics in porous medium and the vehicle-track coupling vibration theory, a numerical method of hydraulic-dynamic coupling was established to evaluate the dynamic responses of saturated roadbed surface layer under the high-speed train loading with the validation by comparing the calculated values and field data. The temporal and spatial characteristics of dynamic behaviors (stress, pore water pressure, seepage velocity, displacement) of saturated roadbed surface layer are fully discussed. Also, the effects of train velocity, permeability, on aforementioned dynamic responses of the saturated roadbed surface layer are evaluated. The study shows that improving the drainage of ballastless track roadbed has a significant effect on minimizing the mud pumping of ballastless track, and the influence zone of hydraulic-mechanical coupling is mainly within 0.1 m of the roadbed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".