Solid mechanics and AI hybrid approach to mitigation of climate change driven railway track buckling

A significant problem for rail networks is prevention of track buckling in hot weather, particularly with climate change leading to extreme and variable conditions. Buckling risk means trains run at slow speeds, reducing network capacity and giving poor customer experience. Traversing buckled track can lead to derailment with severe safety consequences. Data shows buckles are more prevalent for specific track conditions. Individually insignificant factors occur in combination leading to a buckle without an obvious cause. Contributory factors may have stochastic nature through variability in components, installation, their age, loading history or other factors.
Large-scale data collection on the railway network is opening the possibility of Artificial Intelligence (AI) approaches to accompany conventional mechanics in understanding rail infrastructure. An analytical mechanics model of rail buckling considering the track system as a restrained ladder structure is available in Sheffield. In this research it is anticipated this will be extended with a finite element model to more fully capture realistic (and stochastic) behaviour. Alongside this an AI model of buckling will be developed with rail network data. A particular fuzzy-set based methodology has proven to offer fast reliable predictions (in alternative cases 95-97% accuracy) estimating factors influencing results significantly. The joint support of the research by academic supervisors and industry will provide an exceptional opportunity to address the research problem using the latest ideas with potential to deliver real benefits of improved rail system performance.