RAGTIME
Risk based approaches for Asset inteGrity multimodal Transport Infrastructure ManagEment
| Business | Time | Project Coordinator | Funding Scheme |
|---|---|---|---|
| Industry Transport & Infrastructure |
Start date: 09/01/2016 End date: 08/31/2019 |
FUNDACION TECNALIA RESEARCH & INNOVATION | Horizon 2020, Mobility for Growth WorkProgramme |
Challenge
An efficient asset management process is needed to ensure cost-effectiveness in planning, delivery, operation and maintenance of large transport infrastructures network.
Indeed, it has to proper manage asset maintenance activities, determines the life cycle and replacement costs of the assets, assists in identifying financial strategies, thus optimizing capital investments in operation and maintenance.
Currently, the procurement, design, construction and delivery of transport infrastructure, presents the following drawbacks:
- Are not multimodal, not cross-assets, but focused on individual assets
- Are not correctly linked, not being able to exchange information by different stakeholders
- Lack of a common risk based approach and the implementation of resilient concepts throughout the whole life cycle
In this context, the main objective of RAGTIME (a project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 690660) is to develop and validate a system planning software platform, based on standard multiscale data models, able to:
- facilitate a holistic management of large infrastructure, throughout the entire lifecycle
- provide an integrated view of risks (technical, organizational, operational) from procurement to operation and maintenance
- implement risk based models and resilient concepts to mitigate various risks that may arise throughout the life-cycle, with specific attention to natural risks (e.g. landslides, earthquakes, floods, heavy snow, icing, scour)
Approach
In the framework of RAGTIME, RINA is in charge of the development of RAGTIME Software Platform for efficient Asset Management of Transport Infrastructure.
Based on open data models and cloud technology, this platform aims at providing scalable user interfaces in order to deliver information to different users (e.g. Infrastructure Managers, Maintenance
Companies, Competent Authorities), by different devices (e.g. desktop PC, tablet, smartphones), thus increasing the availability and sharing of context-relevant information.
In particular, RINA has been working on data models to allow the management and analysis of data from different sources, including on site sensors, aerial images, data from drones, etc. and to provide the elaborated data to the users at different scales and with different level of detail (e.g. the scale of structural element, the one of the infrastructure, or the one of a complex network).
Moreover, RINA is in charge of the development of the Core Service functions of the platform, so that to provide added value services to the users (e.g. advanced and smart monitoring, predictive maintenance, probabilistic loss/profit estimations, risk-based planning and inspections). The services will be released in the framework of a Platform as a Service (PaaS), so that to ensure modularity, flexibility, interoperability with legacy systems and openness to third party applications via Application Programming Interface (API).
Conclusion
In conclusion, the importance of the correct implementation of Asset Management for transport infrastructure is nowadays worldwide recognize. Thanks to RAGTIME Software Platform we can offer our contribution to:
- Improve the overall process efficiency in Asset Management of Large Infrastructure Projects, thanks to a risk-based approach applied to all phases of the life-cycle (planning, procurement, construction, operation and maintenance)
- Improve stakeholders collaboration and the sharing of context-relevant information across the various life-cycle stages of the infrastructure
- Reduce the lifecycle costs, thanks to a probabilistic approach to the estimation loss/profit in operation and maintenance
- Reduce potential errors and time of intervention by means of predictive maintenance logics, combined with the adoption of smart monitoring solutions
