Dynamic response of soft soils in high-speed rail foundation: in situ measurements and time domain finite element method model
Bibliographic record
Abstract
The dynamic response of soil to vibrations induced by moving trains has been widely studied using in situ measurements. However, few in situ tests have been conducted to measure the resulting vibration of foundation soils, especially for the foundation of high-speed rail (HSR) in a soft area. In this study, a number of field experiments were conducted on Shanghai–Hangzhou HSR in a suburb of Shanghai, China. The testing instruments were installed in foundation soils just beneath the HSR track to measure the vibration induced by trains moving at different speeds. Test results show the frequencies of foundation soil vibration are characterized by the train speed and geometrical features of the trains and slab track. In the frequency domain, the dominant frequency bands for vertical acceleration, velocity, and displacement of foundation soil decrease successively. In the time domain, the magnitudes of vibration levels at different locations in a soil foundation decrease gradually with increasing distance from the track. Furthermore, higher train speed can result in higher vibration level. Based on the field conditions, a three-dimensional dynamic finite–infinite element model is developed in the time domain. It shows the model is capable of capturing the primary characteristics of train-induced vibration in the field.
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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 | 0.001 | 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".