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LINEAR INFRASTRUCTURE EFFICIENCY IMPROVEMENT BY AUTOMATED LEARNING AND OPTIMISED PREDICTIVE MAINTENANCE TECHNIQUES

Objective

INFRALERT aims to develop an expert-based information system to support and automate linear asset infrastructure management from measurement to maintenance. This enfolds the collection, storage and analysis of inspection data, the deduction of interventions to keep the performance of the network in optimal condition, and the optimal planning of maintenance interventions. It will also assess new construction strategic decisions.
The condition of the land transport infrastructure has a big societal and economic relevance, since constraints result in disruptions of service. The demand for surface transport will significantly increase in the next years. Given budget restrictions, a substantial enlargement of the road/rail network in the next decades is doubtful. Besides, the aging infrastructure will require more maintenance interventions which infer normal traffic operation. Therefore, the only way to increase infrastructure capacity for the increased transportation demand is to optimise the performance of the existing infrastructure. This issue is addressed by INFRALERT.
INFRALERT will develop and deploy solutions that enhance the infrastructure performance and adapt its capacity to growing needs by: (i) ensuring the operability under traffic disruptions; (ii) keeping and increasing the availability by optimising operational maintenance interventions and assessing strategic long-term decisions on new construction; and (iii) ensuring service reliability and safety by minimising incidences and failures.
INFRALERT will be directly applicable by Rail and Road Infrastructure Managers in the field of Intelligent Maintenance and long term strategic planning. Two real pilots (for roads and rail transport systems) will be used to validate and demonstrate the results of the research activities. In both cases, extensive data from auscultation campaigns are available since some years ago. The empirical development of the whole project will be based on these pilot cases.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

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Funding Scheme

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RIA - Research and Innovation action

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Call for proposal

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(opens in new window) H2020-MG-2014-2015

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Coordinator

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 661 250,00
Address
HANSASTRASSE 27C
80686 Muenchen
Germany

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Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Research Organisations
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 661 250,00

Participants (6)

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