Railway Inspection and Information Model (RIIM): An Intelligent Decision-Making Tool for Enhanced Infrastructure Management

High-speed railway tracks represent critical infrastructures exposed to cyclic loads of high intensity, arising from the operation of high-speed or heavy-haul trains. Consequently, regular inspections are required to guarantee the safety and efficiency of the transportation system. Traditionally, track inspections were carried out by human operators that manually checked the state of the different track elements. However, this method is not only time-consuming but also incurs significant costs for infrastructure management companies. To meet the increasing demand for high-speed operations and ensure sustainable development in the railway sector, there is a pressing need to shift towards intelligent inspection systems. In this context, the University of Vigo and COPASA are collaborating on the RIIM project (Railway Inspection and Information Model), dedicated to the development of an efficient inspection solution for enhancing the safety of railway infrastructure. This system utilizes vehicle-embedded sensors for autonomous and georeferenced data collection. With this information, statistical data analysis and Deep Learning techniques are applied to systematically assess the health condition of the infrastructure. The outcome is a decision-making tool seamlessly integrated into a Geographic Information System (GIS) for efficient railway maintenance task management and planning. In this work, the main results of this project are outlined as well as the implications for future research and improvements.