Spatial stochastic direct and inverse analysis for the extent of damage in deteriorated RC structures

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

The problem of updating the parameters of a probabilistic model, describing spatially large structures, based on uncertain output information is analyzed. An unscented Kalman filter (UKF) variant is successfully used, although the analysis has not been cast in a filtering format. The performance of the UKF-variant is compared with other generic gradient-free inverse solvers. To reduce the computational demand of the stochastic model, sensitivity analysis for functional inputs and probabilistic homogenization techniques are used. Without loss of generality for this type of problems, the whole process is described along a specific application concerning diffusion phenomena and steel damage in RC.

Original languageEnglish (US)
Pages (from-to)286-296
Number of pages11
JournalComputers and Structures
Volume128
DOIs
StatePublished - Oct 2 2013

Fingerprint

Inverse Analysis
Kalman filters
Kalman Filter
Damage
Steel
Stochastic models
Homogenization
Probabilistic Model
Sensitivity analysis
Updating
Stochastic Model
Sensitivity Analysis
Filtering
Gradient
Output
Demand
Statistical Models

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Modeling and Simulation
  • Materials Science(all)
  • Mechanical Engineering
  • Computer Science Applications

Cite this

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Spatial stochastic direct and inverse analysis for the extent of damage in deteriorated RC structures. / Papakonstantinou, Konstantinos; Shinozuka, M.

In: Computers and Structures, Vol. 128, 02.10.2013, p. 286-296.

Research output: Contribution to journalArticle

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