The University of Southampton
University of Southampton Institutional Repository

Optimization algorithms for system integration

Optimization algorithms for system integration
Optimization algorithms for system integration

This work outlines the optimization algorithms involved in integrating system analysis and measured data collected from a network of sensors. The integration is required for structural health monitoring problems arising in structural dynamics and related to (1) model parameter estimation used for finite element model updating, (2) model-based damage detection in structures and (3) optimal sensor location for parameter estimation and damage detection. These problems are formulated as single- and multi-objective optimization problems of continuous or discrete-valued variables. Gradient-based, evolutionary, hybrid and heuristic algorithms are presented that effectively address issues related to the estimation of multiple local/global solutions and computational complexity arising in single and multi-objective optimization involving continuous and discrete variables.

Bayesian inference, Damage detection, Information entropy, Optimal sensor location, Structural dynamics, Structural identification
514-523
Trans Tech Publications
Papadimitriou, Costas
3be78708-ed90-4a1f-b18e-5fe4ec2c8de6
Ntotsios, Evaggelos
877c3350-0497-4471-aa97-c101df72e05e
Vincenzini, Pietro
Casciati, Fabio
Papadimitriou, Costas
3be78708-ed90-4a1f-b18e-5fe4ec2c8de6
Ntotsios, Evaggelos
877c3350-0497-4471-aa97-c101df72e05e
Vincenzini, Pietro
Casciati, Fabio

Papadimitriou, Costas and Ntotsios, Evaggelos (2008) Optimization algorithms for system integration. Vincenzini, Pietro and Casciati, Fabio (eds.) In CIMTEC 2008 - Proceedings of the 3rd International Conference on Smart Materials, Structures and Systems - Emboding Intelligence in Structures and Integrated Systems. vol. 56, Trans Tech Publications. pp. 514-523 . (doi:10.4028/www.scientific.net/AST.56.514).

Record type: Conference or Workshop Item (Paper)

Abstract

This work outlines the optimization algorithms involved in integrating system analysis and measured data collected from a network of sensors. The integration is required for structural health monitoring problems arising in structural dynamics and related to (1) model parameter estimation used for finite element model updating, (2) model-based damage detection in structures and (3) optimal sensor location for parameter estimation and damage detection. These problems are formulated as single- and multi-objective optimization problems of continuous or discrete-valued variables. Gradient-based, evolutionary, hybrid and heuristic algorithms are presented that effectively address issues related to the estimation of multiple local/global solutions and computational complexity arising in single and multi-objective optimization involving continuous and discrete variables.

This record has no associated files available for download.

More information

Published date: September 2008
Venue - Dates: 3rd International Conference on Smart Materials, Structures and Systems - Embodying Intelligence in Structures and Integrated Systems, CIMTEC 2008, , Acireale, Sicily, Italy, 2008-06-08 - 2008-06-13
Keywords: Bayesian inference, Damage detection, Information entropy, Optimal sensor location, Structural dynamics, Structural identification

Identifiers

Local EPrints ID: 430363
URI: http://eprints.soton.ac.uk/id/eprint/430363
PURE UUID: 18475bae-c0c2-4de9-89d6-73c0bd5c08b2
ORCID for Evaggelos Ntotsios: ORCID iD orcid.org/0000-0001-7382-0948

Catalogue record

Date deposited: 26 Apr 2019 16:30
Last modified: 16 Mar 2024 04:16

Export record

Altmetrics

Contributors

Author: Costas Papadimitriou
Editor: Pietro Vincenzini
Editor: Fabio Casciati

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×