Rail4Future

Reducing CO2 emissions in the transport sector by 90% by 2050 (relative to 1990) is a central goal of the "European Green Deal". This is to be achieved through an "accelerated shift towards sustainable mobility". Here, rail transport, as one of the most environmentally friendly and energy-efficient means of transport, has a decisive role to play in accelerating the reduction of emissions in the transport sector. However, there are still many challenges to be overcome on the way to a sustainable transport turnaround, such as increased innovation and digitization, in order to be able to exploit the full potential of rail.

Even today, the railroad sector benefits from established analytical approaches, numerical methods and tools. However, these are typically focused on individual issues. The understanding of system interactions in the overall context is now to be raised to a new level within the framework of Rail4Future.

By interlinking data from a wide variety of sources and using novel computational models and modeling techniques, the aim is to create a digital image of sections of track. This so-called Digital Twin enables a realistic assessment of the dynamic behavior, condition and long-term changes of the railroad infrastructure, which in turn allows the timely introduction of appropriate countermeasures and generally contributes to an extension of the life cycle of the infrastructure.

To realize this highly complex research task, Rail4Future brings together for the first time an interdisciplinary consortium in which rail operators, industry, scientific partners and universities pool their expertise, domain know-how, methods, competence and the necessary field data.

Digitization and predictive maintenance of existing railroad structures such as bridges or tunnels is a research focus at AIT. Research projects such as Assets4Rail, DEEB-INFRA or AMBITION aim at optimizing maintenance planning, extending the life cycles of structures and thus contributing significantly to a resilient, resource-saving transport system of the future. Within the framework of Rail4Future, AIT is therefore particularly active in the following areas:

• Development of refined methods for reliability assessment and predictive modeling of fatigue-prone bridges
• Determination of track-specific load models based on historical and current rail traffic, for improved forecasting of remaining service life and subsequently as a basis for predictive maintenance planning
• Definition of requirements for an effective, economical, and easy-to-use ("3E") next-generation bridge monitoring and sensing system for fatigue crack detection and monitoring

Project duration: 03/2021 bis 08/2024