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.