Author(s): Ehsan Kazemi; Yiqi Wu; Andrew Nichols; Simon Tait; Jamil Raja
Keywords: Railway drainage; Track flooding; Earthwork failures; Machine learning; Failure risk prediction
Abstract: UK railway drainage systems are managed by a single infrastructure company called Network Rail (NR). These drainage systems are essential for conveying water away from the track and preventing operational and asset failures such as track surface flooding. With a detailed well calibrated hydraulic model of a drainage system, track flooding could be predicted for anticipated rainfall events, but the data does not exist to build accurate hydraulic models for the entire rail network (20,000 miles). Currently, drainage asset data is sparse, infrequently collected, and not organised into systems. In this study, machine learning (ML) algorithms were developed to analyse historical data of track flooding in order to identify plausible linkages between rainfall parameters, drainage asset condition, and track flooding (seen as failures), and to thus develop a predictive tool for drainage system failure causing track flooding. Plausible linkages between various input and output parameters were firstly identified and investigated in a qualitative and efficient way using an unsupervised ML technique, namely Self-Organising Maps (SOM). The plausibility of the identified linkages was further investigated by conducting interviews with experienced members of NR staff, and probable failure mechanisms were identified for track flooding, and presented in the form of failure pathways. Supervised ML algorithms were then employed to objectively quantify these linkages and estimate potential for track flooding failure under different rainfall scenarios using existing asset characterisation and condition data. The developed algorithms were then applied to understand the potential for failure risk for small groups of assets (1/8-mile sections of a UK railway line) for a range of rainfall values to examine under what condition any small system of assets can be expected to fail. Once trained, the model can be used at an operational level, where weather forecasts can be used to identify assets with immediate high risk, or at a strategic level, where all assets could be assessed for risk of failure under typical rainfall conditions, enabling risk ranking of assets for maintenance prioritisation. Ultimately this technique could provide a mechanism for improving drainage system management, reducing the risk of failure, and thus improving the safety and reliability of the UK’s railway network.