Predictive analytics for enhancing performance and safety in continuously welded rail
Last modified: 2024-04-05
Abstract
The modernization of rail infrastructure demands innovative approaches to ensure optimal performance and safety. In present days, railway tracks are increasingly installed with continuous welded rails (CWR) to reduce train vibration and noise.
Unfortunately, CWR’s are prone to lateral buckling, which is a rather complex phenomenon that is observed in a wide range of physical circumstances. Numerous factors affect track buckling, but the rail temperature and the stress-free (neutral) temperature of the CWR are two of the most important factors. Rail temperatures are closely related to air temperature, which is constantly increasing due to global warming.
The Romanian railway regulations and instructions that are dealing with construction and maintenance works on CWR, define the neutral temperature range between 17 and 27 degrees Celsius for the entire railway network of the country. However, we must bear in mind that Romania, likewise several countries in the EU, has both a quite divers climate and geographical relief. In this condition, the temperature variations can generate excessive thermally induced axial forces, leading in most cases to destabilization or tensile fracture of the CWR track. One of the main concerns of Railway Administrations is to determine the buckling temperature of the CWR in different sections of the railway network. This is not an easy task, due to the several parameters which are affecting the buckling temperature such as: rail’s neutral temperature, misalignment of the rails and ballast resistance.
This paper introduces a comprehensive framework for predictive analytics by presenting a model of probabilistic buckling analysis for CWR tracks based on rail temperature predictions using different wayside monitoring equipment. By adopting these models, rail operators will be able to proactively address issues before they escalate, optimize their maintenance schedules, reduce downtimes, and minimize the risk of accidents.
Unfortunately, CWR’s are prone to lateral buckling, which is a rather complex phenomenon that is observed in a wide range of physical circumstances. Numerous factors affect track buckling, but the rail temperature and the stress-free (neutral) temperature of the CWR are two of the most important factors. Rail temperatures are closely related to air temperature, which is constantly increasing due to global warming.
The Romanian railway regulations and instructions that are dealing with construction and maintenance works on CWR, define the neutral temperature range between 17 and 27 degrees Celsius for the entire railway network of the country. However, we must bear in mind that Romania, likewise several countries in the EU, has both a quite divers climate and geographical relief. In this condition, the temperature variations can generate excessive thermally induced axial forces, leading in most cases to destabilization or tensile fracture of the CWR track. One of the main concerns of Railway Administrations is to determine the buckling temperature of the CWR in different sections of the railway network. This is not an easy task, due to the several parameters which are affecting the buckling temperature such as: rail’s neutral temperature, misalignment of the rails and ballast resistance.
This paper introduces a comprehensive framework for predictive analytics by presenting a model of probabilistic buckling analysis for CWR tracks based on rail temperature predictions using different wayside monitoring equipment. By adopting these models, rail operators will be able to proactively address issues before they escalate, optimize their maintenance schedules, reduce downtimes, and minimize the risk of accidents.
Keywords
Continuously welded rail (CWR), rail temperature, neutral temperature, lateral buckling, buckling prediction, track parameters