The SAFE-10-T project will develop a Safety Framework to ensure high safety performance while allowing longer life-cycles for critical infrastructure across the road, rail and inland waterway transportation modes. Moving from considering critical infrastructure such as bridges, tunnels and earthworks as inert objects to being intelligent (self-learning) objects, the SAFE-10-T project will provide a means of virtually eradicating sudden failures. The project will employ a data-driven approach to provide a decision support tool to infrastructure managers that will aid them with making decisions that minimize risk.
|Duration:||May 1, 2017 - Apr 30, 2020|
Gavin & Doherty Geosolutions Ltd (Ireland)
Roughan & O'Donovan Innovative Solutions Ltd (Ierland)
Technische Universität Delft (Netherlands)
TRL Ltd (United Kingdom)
Network Rail Infrastructure Ltd (United Kingdom)
Virtus IT Ltd (United Kingdom)
Infrastructure Management Consultants GmbH (Switzerland)
Sveuciliste Sveučilište u Zagrebu Građevinski fakultet (Croatia)
HŽ Infrastruktura d.o.o. (Croatia)
INFRA PLAN KONZALTING j.d.o.o. (Croatia)
Technische Universität Berlin (Germany)
Istituto di Sociologia Internazionale di Gorizia I.S.I.G (Italy)
Forum Des Laboratoires Nationaux Europeens De Recherche Routiere (Belgium)
|Research area:||Hardware Systems Software Systems|
|Grant number:||Grant agreement No. 690660|
The SAFE-10-T project will develop a Safety Framework to ensure high safety performance while allowing longer life-cycles for critical infrastructure across the road, rail and inland waterway modes. Moving from considering critical infrastructure such as bridges, tunnels and earthworks as inert objects to being intelligent (self-learning objects) the SAFE-10-T project will provide a means of virtually eradicating sudden failures. This will be achieved by:
- The Safety framework will incorporate remote monitoring data stored in a BIM model that feeds into a decision support framework (DST) that enables decisions to be made automatically with maintenance prioritised for elements exhibiting stress.
- A major advance that will be achieved in the project is that the algorithms at an object level and at a network level will incorporate machine learning to train the system to evolve with time using available monitoring data.
- A trans-disciplinary approach with experts in Artificial Intelligence and big data management working with owners, engineers with expertise in risk and modelling and sociologists to make decisions.
- Our major European infrastructure managers (Rijkswaterstaat for roads and inland waterways and Network Rail) will undertake demonstration projects at critical interchanges and nodes of the TEN-T transport network.
The project will achieve significant impact in asset management by:
- By moving to intelligent objects that communicate their safety condition during extreme events we will provide a means of virtually eradicating sudden catastrophic failure of infrastructure objects.
- The project will use Open Linked Data formats to manage all data and inputs from other sources. Mitigation actions can be taken and warnings of the increased risk level can be transmitted to other agencies and the public.
- Demonstrate the concept of fully interconnected transport networks on the TEN-T network.