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Improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques (INFRALERT)
CEMOSA.
CEMOSA.
Fraunhofer Inst Verkehrs & Infrastruktursyst IV.
Fraunhofer Inst Verkehrs & Infrastruktursyst IV.
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2017 (English)In: IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981, E-ISSN 1757-899X, Vol. 236Article in journal (Refereed) Published
Abstract [en]

The on-going H2020 project INFRALERT aims to increase rail and road infrastructure capacity in the current framework of increased transportation demand by developing and deploying solutions to optimise maintenance interventions planning. It includes two real pilots for road and railways infrastructure. INFRALERT develops an ICT platform ( the expert-based Infrastructure Management System, eIMS) which follows a modular approach including several expert-based toolkits. This paper presents the methodologies and preliminary results of the toolkits for i) nowcasting and forecasting of asset condition, ii) alert generation, iii) RAMS & LCC analysis and iv) decision support. The results of these toolkits in a meshed road network in Portugal under the jurisdiction of Infraestruturas de Portugal (IP) are presented showing the capabilities of the approaches.

Place, publisher, year, edition, pages
Prague, Czech Republic, 2017. Vol. 236
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-67204DOI: 10.1088/1757-899X/236/1/012105ISI: 000417428700105OAI: oai:DiVA.org:ltu-67204DiVA: diva2:1172262
Conference
BESTInfra 2017, Czech Technical University, Prague, Czech Republic, September 21-22 2017
Projects
INFRALERT
Note

Konferensbidrag i tidskrift

Available from: 2018-01-09 Created: 2018-01-09 Last updated: 2018-01-09Bibliographically approved

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Odelius, JohanThaduri, Adithya
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