Forecasting ballast performance under fast heavy-haul trains using an analytical-machine learning track (AMLT) model
Bibliographic record
Abstract
For ballasted railway tracks catering to fast heavy-haul trains, it is pertinent to consider the dynamic amplification of ballast permanent response with speeds to optimise track maintenance. This paper presents an analytical-machine learning track model to analyse the influence of heavy-haul trains operating at different speeds on permanent vertical strains ( ε v ) and breakage of ballast (BBI). Using a physics-based analytical model, the elasto-dynamic response considering Rayleigh-wave propagation is captured. This response is then used as an input to data-driven models for ε v and BBI developed using a Genetic Algorithm integrated with an artificial neural network, trained with past laboratory data using relevant input parameters. Results showed that both ε v and BBI increase with train speeds, and their amplification is significantly greater than the dynamic stress amplification factor. By using operational thresholds for ε v and BBI, a new performance-based limiting speed is proposed which can be used as an alternative to critical speed for heavy-haul trains. In contrast to critical speed, the limiting speed is much lower and is also dependent on the axle load and the age of ballast. Furthermore, the influence of stiff subgrade and higher confining stress on limiting speeds are presented with implications to practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".