Ground track response behaviour induced by train speed using seismic wave method
Bibliographic record
Abstract
Abstract Ground track response analysis is an alternative method utilized to investigate the influence of ground-borne vibrations induced by speed train on track. This study intended to get an understanding about the responsive of ground towards vibration induced from moving train. To facilitate this study, non-destructive seismic wave method was performed using new application, Sirius M instrument to identify the peak vertical acceleration generated at various running speed and track locations. The result shows the vertical acceleration data signalized from transition wave generate through passing Electric Train Service (ETS) with maximum 140km.hr−1 speed train is higher than commuter with 120km.hr−1 speed train which are 1.935m.s−2 and 1.051m.s−2 respectively. Analysis of vertical acceleration data based on different track locations corresponding to the ETS speed resulting higher peak acceleration at stable track, KM21 compared to settlement susceptible track, KM20.75 which are 6.565m.s−2 and 1.935m.s−2 respectively. The values obtained from this study indicated ground-borne vibration influenced by speed of train and different type of embankment foundation. This data can be used to assess the influences of train type and speed. Moreover, this on-site ground response measurement is potentially useful as an alternative method to determine the soil stiffness which provide an indication to the possible problematic ground susceptible to the settlement.
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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) | 0.002 | 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".