Estimation of vertical bending stress in rails using train-mounted vertical track deflection measurement systems
Bibliographic record
Abstract
This paper presents a new methodology for the estimation of bending stresses over long sections of rails from the vertical track deflections measured using train-mounted instrumentation. The basis of this method is to apply mathematical correlations between the rail deflections and stresses to interpret the deflection measurements. A new finite element modeling method was developed to investigate mathematical correlations between the rail deflections and stresses for different ranges of track modulus. The stochastic nature of the track modulus, as one of the dominant factors influencing rail deflections and stresses, was simulated. The rail responses to applied loads were then calculated and compared for scenarios of constant and variable track modulus values. The study resulted in a detailed framework that can be employed to estimate rail bending stresses from train-mounted vertical track deflection measurements. This framework allows the estimation of the probability distributions of maximum tensile and compressive bending stresses in the rail head and base, which are necessary for calculating the rail reliability under applied loading.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".