Prediction inaccuracies and uncertainties associated with common assumptions in modelling vibration from underground railways
Bibliographic record
Abstract
Underground railways produce significant ground-borne vibration which 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 four commonly disregarded aspects of the underground railway environment and their respective effects on vibration prediction values: the second (twin) tunnel, track with discontinuous slabs, soil inhomogeneity, and irregular contact at the tunnel-soil interface. Results suggest that accounting for each of these simplifying assumptions can result in predictions which vary from the simplified cases by at least ±5dB and potentially up to 20dB. 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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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 | 0.000 | 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) | 0.000 | 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".