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Structural model updating and prediction variability using Pareto optimal models

Structural model updating and prediction variability using Pareto optimal models
Structural model updating and prediction variability using Pareto optimal models
A multi-objective identification method for structural model updating based on modal residuals is presented. The method results in multiple Pareto optimal structural models that are consistent with the experimentally measured modal data and the modal residuals used to measure the discrepancies between the measured and model predicted modal characteristics. These Pareto optimal models are due to uncertainties arising from model and measurement errors. The relation between the multi-objective identification method and the conventional single-objective weighted modal residuals method for model updating is investigated. Using this relation, an optimally weighted modal residuals method is also proposed to rationally select the most preferred model among the alternative multiple Pareto optimal models for further use in structural model prediction studies. Computational issues related to the reliable solution of the resulting multi-objective and single optimization problems are addressed. The model updating methods are compared and their effectiveness is demonstrated using experimental results obtained from a three-story laboratory structure tested at a reference and a mass modified configuration. The variability of the Pareto optimal models and their associated response prediction variability are explored using two structural model classes, a simple 3-DOF model class and a higher fidelity 546-DOF finite element model class. It is demonstrated that the Pareto optimal structural models and the corresponding response and reliability predictions may vary considerably, depending on the fidelity of the model class and the size of measurement errors.
model updating, structural identification, multi-objective optimization, pareto optimality, reliability
0045-7825
138-149
Christodoulou, Konstantinos
9a031944-cd8a-4bd3-a1bf-5fe36c20d98a
Ntotsios, Evaggelos
877c3350-0497-4471-aa97-c101df72e05e
Papadimitriou, Costas
3be78708-ed90-4a1f-b18e-5fe4ec2c8de6
Panetsos, Panagiotis
9da8645c-ca08-4db4-a110-3cdc8d6cdd84
Christodoulou, Konstantinos
9a031944-cd8a-4bd3-a1bf-5fe36c20d98a
Ntotsios, Evaggelos
877c3350-0497-4471-aa97-c101df72e05e
Papadimitriou, Costas
3be78708-ed90-4a1f-b18e-5fe4ec2c8de6
Panetsos, Panagiotis
9da8645c-ca08-4db4-a110-3cdc8d6cdd84

Christodoulou, Konstantinos, Ntotsios, Evaggelos, Papadimitriou, Costas and Panetsos, Panagiotis (2008) Structural model updating and prediction variability using Pareto optimal models. [in special issue: Computational Methods in Optimization Considering Uncertainties] Computer Methods in Applied Mechanics and Engineering, 198 (1), 138-149. (doi:10.1016/j.cma.2008.04.010).

Record type: Article

Abstract

A multi-objective identification method for structural model updating based on modal residuals is presented. The method results in multiple Pareto optimal structural models that are consistent with the experimentally measured modal data and the modal residuals used to measure the discrepancies between the measured and model predicted modal characteristics. These Pareto optimal models are due to uncertainties arising from model and measurement errors. The relation between the multi-objective identification method and the conventional single-objective weighted modal residuals method for model updating is investigated. Using this relation, an optimally weighted modal residuals method is also proposed to rationally select the most preferred model among the alternative multiple Pareto optimal models for further use in structural model prediction studies. Computational issues related to the reliable solution of the resulting multi-objective and single optimization problems are addressed. The model updating methods are compared and their effectiveness is demonstrated using experimental results obtained from a three-story laboratory structure tested at a reference and a mass modified configuration. The variability of the Pareto optimal models and their associated response prediction variability are explored using two structural model classes, a simple 3-DOF model class and a higher fidelity 546-DOF finite element model class. It is demonstrated that the Pareto optimal structural models and the corresponding response and reliability predictions may vary considerably, depending on the fidelity of the model class and the size of measurement errors.

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e-pub ahead of print date: 17 April 2008
Published date: 15 November 2008
Keywords: model updating, structural identification, multi-objective optimization, pareto optimality, reliability
Organisations: Dynamics Group

Identifiers

Local EPrints ID: 372217
URI: https://eprints.soton.ac.uk/id/eprint/372217
ISSN: 0045-7825
PURE UUID: 524c8ac2-7ec2-49f5-adec-5af41f21062e
ORCID for Evaggelos Ntotsios: ORCID iD orcid.org/0000-0001-7382-0948

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Date deposited: 03 Dec 2014 15:31
Last modified: 12 Nov 2019 01:37

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