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SUMMARY:Methods for information transfer within populations of structures 
 - Dr Tina Dardeno\, University of Sheffield
DTSTART:20240426T150000Z
DTEND:20240426T160000Z
UID:TALK213313@talks.cam.ac.uk
CONTACT:46601
DESCRIPTION:Population-based structural health monitoring (PBSHM) aims to 
 share valuable information within a population\, such as normal- and damag
 e-condition data\, to improve predictions regarding the members’ health 
 states. Homogeneous populations\, comprised of nominally-identical structu
 res\, and heterogeneous populations\, characterised by greater disparities
  among the members\, both exhibit dynamic variability because of factors s
 uch as material properties\, geometry\, boundary conditions\, and environm
 ental effects.\n\nMany SHM strategies rely on monitoring these dynamic pro
 perties\, so benign variations pose challenges to system implementation an
 d generalisation. Hierarchical (multilevel) Bayesian models with partial p
 ooling have been shown to be effective for homogeneous systems. These mode
 ls simultaneously consider population-level similarities and individual-le
 vel differences\, leading to more robust statistical inferences and reduce
 d variance in parameter estimates\, particularly when data are sparse. Het
 erogeneous populations have members that are farther apart in the feature 
 space\, and information transfer within these populations is considerably 
 more challenging. However\, geometrical transfer approaches (e.g.\, geodes
 ic flows) are an exciting prospect\, as they are naturally equipped to cap
 ture the complex curvature of these spaces.\n\nThis talk outlines the appl
 ication of hierarchical Bayesian models to SHM and PBSHM via experimental 
 case studies. Preliminary findings on a transfer method for heterogeneous 
 populations are also introduced.
LOCATION:JDB Seminar Room\, CUED
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