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SUMMARY:On the coalescence of structural mechanics models with data for co
 ndition monitoring
DTSTART:20210408T121500
DTEND:20210408T131500
DTSTAMP:20260506T124848Z
UID:b3adb6655427b024c48b794f18e19516aef95a700cd6baacc00740f5
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Eleni Chatzi Chaire of Structural Mechanics\, ETH Zurich
 \nAbstract: The monitoring of the condition of structural systems operati
 ng under diverse dynamic loads involves the tasks of simulation (forward e
 ngineering)\, identification (inverse engineering) and maintenance/control
  actions. The efficient and successful implementation of these tasks is ho
 wever non-trivial\, due to the ever-changing nature of these systems\, the
  variability in their interactive environment\, and the polymorphic uncert
 ainties involved. Structural Health Monitoring (SHM) attempts to tackle th
 ese challenges by exploiting information stemming from sensor networks. SH
 M comprises a hierarchy across levels of increasing complexity aiming to i
 ) detect damage\, ii) localize and iii) quantify damage\, and iv) finally 
 offer a prognosis over the system’s residual life.\nWhen considering hig
 her levels in this hierarchy\, including damage assessment and even perfor
 mance prognosis\, purely data-driven methods are found to be lacking. For 
 higher-level SHM tasks\, or for furnishing a digital twin of a monitored s
 tructure\, it is necessary to integrate the knowledge stemming from physic
 s-based representations\, relying on the underlying mechanics. This talk d
 iscusses implementation of such a hybrid approach to SHM for tackling the 
 aforementioned challenges. Among other topics\, we will discuss the potent
 ial and limitations of purely data-driven schemes\, and the benefits stemm
 ing from infusion of data with reduced order structural mechanics models\,
  in support of diagnostics and prognostics for engineered systems.
LOCATION:https://ethz.zoom.us/j/94817809233?pwd=N0pzbnQwSFFTQnVPcVR3SkNrd2
 9OQT09
STATUS:CONFIRMED
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