Objective
Structural Health Monitoring (SHM) is expected to play a predominant role in the management of the transport infrastructure. Yet, SHM techniques continue to rely on point-based, as opposed to spatial, sensing requiring a dense network of these point-sensors increasing considerably the monitoring cost. Additionally, commercially available, strain sensors cannot measure strains beyond 1% to 2% and, thus, are not able to provide an alarm for an imminent catastrophe.
SENSKIN aims to:
(a) develop a dielectric-elastomer and micro-electronics-based skin-like sensing solution for the structural monitoring of the transport infrastructure that will offer spatial sensing of reversible (repeated) strains in the range of 0.012% to more than 10%, that requires little power to operate, is easy to install on an irregular surface, is low cost compared to existing sensors, allows simple signal processing and includes the ability of self-monitoring and self-reporting.
(b) use the new and emerging technology of Delay Tolerant Network to secure that strain measurements acquired through the 'sensing skin' will reach the base station even under extreme environmental conditions and natural disaster events such as, high winds or an earthquake, where some communication networks could become inoperable.
(c) develop a Decision-Support-System for proactive condition-based structural intervention under operating loads and intervention after extreme events. It will be based on an accurate structural assessment based on input from the strain sensors in (a) above and will examine the life-cycle economic, social and environmental implications of the feasible rehabilitation options and the resilience of the infrastructure to future changes in traffic demand that these options offer.
(d) implement the above in the case of bridges and test, refine, evaluate and benchmark the monitoring system (integrated a and b) and package (integrated a, b and c) on actual bridges.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
106 82 ATHINA
Greece
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Participants (14)
14469 Potsdam
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57001 THESSALONIKI
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10707 Berlin
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
00195 ROMA
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
Participation ended
691 00 KOMOTINI
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14452 ATHINA
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70174 Stuttgart
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RG40 3GA Wokingham
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03113 KYIV
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1000 Bruxelles / Brussel
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
03 302 Warszawa
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01454 Radeberg
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
06100 YUCETEPE ANKARA
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11745 Athens
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.