NThe SAFE-10-T project developed a Safety Framework to ensure high safety performance while allowing longer life-cycles for critical infrastructure across the road, rail, and inland waterway modes. It transitioned from considering critical infrastructure such as bridges, tunnels, and earthworks as inert objects to being intelligent (self-learning objects). The SAFE-10-T project provided a means of virtually eradicating sudden failures by:
- Incorporating remote monitoring data stored in a BIM model into a decision support framework (DST).
- Enabling automatic decision-making with maintenance prioritized for elements exhibiting stress.
- Achieving a major advance by incorporating machine learning algorithms at an object level and at a network level.
- Training the system to evolve with time using available monitoring data.
The project involved a trans-disciplinary approach with experts in Artificial Intelligence and big data management working alongside owners, engineers with expertise in risk and modeling, and sociologists to make decisions. Major European infrastructure managers (Rijkswaterstaat for roads and inland waterways and Network Rail) undertook demonstration projects at critical interchanges and nodes of the TEN-T transport network.
The project achieved significant impact in asset management by:
- Moving to intelligent objects that communicated their safety condition during extreme events, providing a means of virtually eradicating sudden catastrophic failure of infrastructure objects.
- Using Open Linked Data formats to manage all data and inputs from other sources.
- Taking mitigation actions and transmitting warnings of the increased risk level to other agencies and the public.
The project also aimed to demonstrate the concept of fully interconnected transport networks on the TEN-T.