Development of a Condition Assessment Rating System and Prediction Model for Railway Tracks
Bibliographic record
Abstract
Canada has an extensive rail network spanning 45,000 kilometres. The railway system plays a crucial role in serving almost every sector of the Canadian economy. Primarily, it transports freight to and from the U.S. and global markets through coastal ports. However, failures in the railway infrastructure can have severe safety and financial consequences. In 2023, 43.13% of main-track derailments were attributed to track defects, according to the Transportation Safety Board of Canada. These defects, including issues with track geometry and component failures, underline the need for better track condition monitoring and maintenance to prevent derailments. This research aims to address this need by developing a comprehensive rating system for evaluating the condition of ties and rail fastening components and machine learning models to predict future track conditions. While traditional condition assessment ratings have relied on subjective evaluations and considered components separately, this study proposes a Tie and Rail Fastening system that evaluates the condition of ties, tie plates, and spikes. Domain expertise was incorporated through the Analytic Hierarchy Process (AHP) to prioritize the importance of various defects. The resulting weighting system provides a more detailed and integrated approach compared to existing rating methods, which primarily focus on crack size. Machine learning models, including Random Forest, XGBoost, and Cat Boost, were employed to predict future conditions, such as defect tags, amplitude, and length. These models achieved a 95% accuracy for detecting defect tags and a 75% accuracy when predicting defect tags based on predicted amplitude. On the one hand, the proposed tie and rail fastening rating system can improve the prioritization of future rail maintenance works. On the other hand, the proposed machine learning models can improve the planning of future maintenance by offering better tools for monitoring and predicting track conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".