Prediction uncertainties and inaccuracies resulting from common assumptions in modelling vibration from underground railways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Underground railways produce significant ground-borne vibration that is reported to disturb people living or working near subways. Designers and engineers use numerical models to predict vibration levels so as to meet the increasingly strict vibration standards. These models commonly include simplifying assumptions to reduce the complexity and cost of the simulation. This paper reviews six commonly disregarded aspects of the underground railway environment and their respective effects on vibration prediction values: a second (twin) tunnel, piled foundations, track with discontinuous slabs, soil inhomogeneity, inclined soil layers, and irregular contact at the tunnel–soil interface. Results suggest that accounting for each of these simplifying assumptions can result in predictions that vary from the simplified cases by at least 5 dB and potentially up to 20 dB. This is a significant level of uncertainty and should be considered when estimating the predictive accuracy of numerical models using simplifying assumptions.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it