Estimation of track modulus over long distances using artificial neural networks
Bibliographic record
Abstract
Evaluating the railway track structure and identifying the problematic locations with the urgent need of repair along the thousands of miles of tracks have always been a challenge to the railroad industry. Track foundation modulus (also referred to as the track modulus) is one of the main parameters that affect the track performance, and thus, quantifying its magnitude and variation along the track has a potential to be a significant addition to the current methods for evaluating the track structure. Track modulus can be quantified by measuring the deflection of the rail when subjected to a known applied load. Hence, train-mounted vertical track deflection (VTD) measurement systems that have been developed over the past decades, present a great opportunity to estimate track modulus and its variation over long distances. This paper presents a new methodology for quantifying the track modulus average over track windows using VTD measurements. In this study, finite element modeling was used to simulate the track structure with stochastically varying track modulus. Various track modulus distributions were considered and the deflection of the rail under moving load was calculated at the predefined intervals along the track. The mathematical correlation between the rail deflection and track modulus over track windows was then studied using artificial neural networks. Numerical results suggest the average of track modulus over track windows can be successfully estimated using VTD measurements.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".