Simulation-based evaluation of maintenance strategies using look-up tables
Bibliographic record
Abstract
A computationally efficient approach, that uses pre-calculated simulation-based look-up tables of long-term track degradation to evaluate and optimise maintenance strategies, is proposed. The methodology combines iterative multibody simulations – to model the evolution of rail profiles and the distribution of rolling-contact fatigue under realistic conditions – and an optimisation procedure aimed at minimising the lifecycle costs. The research explores the interplay between long-term track degradation, maintenance interventions, and lifecycle costs. The approach is illustrated through an application to a small radius curve on Sweden’s Iron-ore line, focusing on rail grinding and track gauge correction. Optimal renewal and maintenance strategies are analysed for different scenarios such as rail profiles and axle loads. The results highlight the importance of gauge widening rates, rail profiles and axle loads in track deterioration as well as their effects on the asset’s lifecycle costs. A sensitivity analysis is conducted to study the impact of different parameters such as maintenance and renewal costs, and gauge widening rates. The proposed approach offers infrastructure managers a systematic method for efficient planning of maintenance interventions on various assets. Future work could explore applications to larger infrastructure areas, encompassing multiple assets with a complex composition of tangent and curved track sections as well as switches & crossings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".