Relationships and statistics between various vibration metrics, derived from a large set of measured train pass-bys
Bibliographic record
Abstract
The impacts of groundborne vibration (GBV) and sound (GBS) induced by railway infrastructure are assessed across the world through different metrics. Many national and international standards can be found for assessing GBV, based either on acceleration (UK, Spain, etc.) or velocity metrics (Germany, USA, etc.). Various frequency and/or time-weightings can be used, and different quantities (running RMS or highest levels) are also considered. Direct comparison between the various assessment criteria and measured metrics is, therefore, difficult. Although no international standard defines GBS criteria, many guidelines suggest such a criterion through a relationship between GBS levels and vibration velocity levels on room surfaces. Different quantities are often used, even for neighboring projects: London’s Crossrail (now Elizabeth Line) and Northern Line extension impact assessments both used maximum vibration velocity levels but with different time weighting (slow and fast, respectively). Arup has amassed a large dataset of vibration measurements through its involvement in many railway schemes over the years. For each of the thousands of train pass-bys, measured at different distances, infrastructure types, operation, and ground conditions, the most common European and North American GBS and GBV metrics are derived and compared to each other to develop statistical relationships and associated uncertainties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".