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SUMMARY:On the fusion of data and models\; the hybrid path to Diagnosis 
 &amp\; Prognosis for Infrastructure - Eleni Chatzi - ETH Zurich
DTSTART:20210604T140000Z
DTEND:20210604T150000Z
UID:TALK160906@talks.cam.ac.uk
CONTACT:Mishael Nuh
DESCRIPTION:The monitoring of the condition of structural systems operatin
 g under diverse dynamic loads involves the tasks of simulation (forward en
 gineering)\, identification (inverse engineering) and maintenance/control 
 actions. The efficient and successful implementation of these tasks is how
 ever non-trivial\, due to the ever-changing nature of these systems\, the 
 variability in their interactive environments\, and the polymorphic uncert
 ainties involved. Structural Health Monitoring (SHM) attempts to tackle t
 hese challenges by exploiting information stemming from sensor networks.\n
 \nSHM comprises a hierarchy across levels of increasing complexity aiming 
 to i) detect damage\, ii) localize and iii) quantify damage\, and iv) fina
 lly offer a prognosis over the system's residual life. When considering hi
 gher levels in this hierarchy\, including damage assessment and even perfo
 rmance prognosis\, purely data-driven methods are found to be lacking. For
  higher-level SHM tasks\, or for furnishing a virtualization of a monito
 red structure\, it is necessary to integrate the knowledge stemming from p
 hysics-based representations\, relying on the underlying mechanics. This t
 alk discusses implementation of such a hybrid approach to SHM for tackling
  the aforementioned challenges with particular focus on applications for w
 ind turbine structures.
LOCATION:Zoom (email structures-admin@eng.cam.ac.uk for link)
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