Investigation into the dynamic stress of pile-supported embankment caused by moving train loads
Bibliographic record
Abstract
This study presents a full-scale model of pile-supported embankment with ballasted track-bed. The monitored dynamic stresses enabled the time histories, frequency contents, and distributions to be investigated under different train speeds and axle loads. Results show that the dynamic stress peaks representing the axle effect gradually fell off along depth, in agreement with the filtered frequencies in the Fast Fourier Transform analysis. The great train speeds brought more significant dynamics, slowing down the diffusion of the dynamic stress peaks, while the axle loads rather affect the intensity of dynamic stress. The critical height of dynamic soil arching grew with the increase of train speeds, but the dynamic pile efficacies (the proportion of dynamic load carried by pile) under various train speeds and axle loads were stable at 83.5%. Compared to the ballasted track, the ballastless track showed a dynamic stress dispersing more quickly due to its higher stiffness. Besides, the axle-induced dynamic stress peaks were not visible on the dynamic stress history in the case of ballastless track. Based on the test results and the Boussinesq stress solution, an analytical method was developed to estimate the dynamic stress distribution within pile-supported embankments with good accuracy, taking into account the dynamic stress factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".