The University of Southampton
University of Southampton Institutional Repository

Optimal experimental design for structural health monitoring applications

Optimal experimental design for structural health monitoring applications
Optimal experimental design for structural health monitoring applications

Successful structural health monitoring and condition assessment depends to a large extent on the sensor and actuator networks place on the structure as well as the excitation characteristics. An optimal experimental design methodology deals with the issue of optimizing the sensor and actuator network, as well as the excitation characteristics, such that the resulting measured data are most informative for monitoring the condition of the structure. Information theory based approaches have been developed to provide rational solutions to several issues encountered in the problem of selecting the optimal sensor configuration. The optimal sensor configuration is taken as the one that maximizes some norm (determinant or trace) of the Fisher information matrix. Papadimitriou et al. (2000) introduced the information entropy norm as the measure that best corresponds to the objective of structural testing, which is to minimize the uncertainty in the model parameter estimates. An important advantage of the information entropy measure is that it allows one to make comparisons between sensor configurations involving a different number of sensors in each configuration. Furthermore, it has been used to design the optimal characteristics of the excitation (e.g. amplitude and frequency content) useful in the identification of linear and strongly nonlinear models. The optimal sensor placement strategies depend on the class of mathematical models selected to represent structural behavior as well as the model parameterization within the model class. However, it is often desirable to use the measured data for selecting the most appropriate model class from a family of alternative model classes chosen by the analyst to represent structural behavior. Such classes may be linear (modal models or finite element models), nonlinear elastic or inelastic, each one involving different number of parameters. Model class selection is also important for damage detection purposes for which the location and severity of damage are identified using a family of model classes with each model class monitoring a specific region in a structure (Papadimitriou & Katafy-giotis 2004) or incorporating different mechanisms of damage. The objective in this work is to optimise the number and location of sensors in the structure such that the resulting measured data are most informative for estimating the parameters of a family of mathematical model classes used for structural identification and damage detection purposes. Theoretical and computational issues arising in optimal experimental design are addressed. The problem is formulated as a multi-objective optimization problem of finding the Pareto optimal sensor configurations that simultaneously minimize appropriately defined information entropy indices related to monitoring multiple probable damage scenarios. Asymptotic estimates for the information entropy, valid for large number of measured data, are used to rigorously justify that the selection of the optimal experimental design can be based solely on nominal structural models associated with the probable damage scenarios, ignoring the details of the measured data that are not available in the experimental design stage. Heuristic algorithms are proposed for constructing effective, in terms of accuracy and computational efficiency, sensor configurations. Damage detection results on a shear model of a building structure are used to illustrate the theoretical developments.

CRC Press
Lam, H. F.
dc26b25c-f510-4112-a766-f87971a8f9c0
Papadimitriou, C.
3be78708-ed90-4a1f-b18e-5fe4ec2c8de6
Ntotsios, E.
877c3350-0497-4471-aa97-c101df72e05e
Spencer, B.F.
Tomizuka, M.
Yun, C.B.
Chen, W.M.
Chen, R.W.
Lam, H. F.
dc26b25c-f510-4112-a766-f87971a8f9c0
Papadimitriou, C.
3be78708-ed90-4a1f-b18e-5fe4ec2c8de6
Ntotsios, E.
877c3350-0497-4471-aa97-c101df72e05e
Spencer, B.F.
Tomizuka, M.
Yun, C.B.
Chen, W.M.
Chen, R.W.

Lam, H. F., Papadimitriou, C. and Ntotsios, E. (2008) Optimal experimental design for structural health monitoring applications. Spencer, B.F., Tomizuka, M., Yun, C.B., Chen, W.M. and Chen, R.W. (eds.) In World Forum on Smart Materials and Smart Structures Technology : Proceedings of SMSST'07, World Forum on Smart Materials and Smart Structures Technology (SMSST'07), China, 22-27 May, 2007. CRC Press. 1 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Successful structural health monitoring and condition assessment depends to a large extent on the sensor and actuator networks place on the structure as well as the excitation characteristics. An optimal experimental design methodology deals with the issue of optimizing the sensor and actuator network, as well as the excitation characteristics, such that the resulting measured data are most informative for monitoring the condition of the structure. Information theory based approaches have been developed to provide rational solutions to several issues encountered in the problem of selecting the optimal sensor configuration. The optimal sensor configuration is taken as the one that maximizes some norm (determinant or trace) of the Fisher information matrix. Papadimitriou et al. (2000) introduced the information entropy norm as the measure that best corresponds to the objective of structural testing, which is to minimize the uncertainty in the model parameter estimates. An important advantage of the information entropy measure is that it allows one to make comparisons between sensor configurations involving a different number of sensors in each configuration. Furthermore, it has been used to design the optimal characteristics of the excitation (e.g. amplitude and frequency content) useful in the identification of linear and strongly nonlinear models. The optimal sensor placement strategies depend on the class of mathematical models selected to represent structural behavior as well as the model parameterization within the model class. However, it is often desirable to use the measured data for selecting the most appropriate model class from a family of alternative model classes chosen by the analyst to represent structural behavior. Such classes may be linear (modal models or finite element models), nonlinear elastic or inelastic, each one involving different number of parameters. Model class selection is also important for damage detection purposes for which the location and severity of damage are identified using a family of model classes with each model class monitoring a specific region in a structure (Papadimitriou & Katafy-giotis 2004) or incorporating different mechanisms of damage. The objective in this work is to optimise the number and location of sensors in the structure such that the resulting measured data are most informative for estimating the parameters of a family of mathematical model classes used for structural identification and damage detection purposes. Theoretical and computational issues arising in optimal experimental design are addressed. The problem is formulated as a multi-objective optimization problem of finding the Pareto optimal sensor configurations that simultaneously minimize appropriately defined information entropy indices related to monitoring multiple probable damage scenarios. Asymptotic estimates for the information entropy, valid for large number of measured data, are used to rigorously justify that the selection of the optimal experimental design can be based solely on nominal structural models associated with the probable damage scenarios, ignoring the details of the measured data that are not available in the experimental design stage. Heuristic algorithms are proposed for constructing effective, in terms of accuracy and computational efficiency, sensor configurations. Damage detection results on a shear model of a building structure are used to illustrate the theoretical developments.

This record has no associated files available for download.

More information

Published date: 1 December 2008
Venue - Dates: World Forum on Smart Materials and Smart Structures Technology, SMSST 07, , Chongqing, Nanjing, China, 2007-05-22 - 2007-05-27

Identifiers

Local EPrints ID: 430365
URI: http://eprints.soton.ac.uk/id/eprint/430365
PURE UUID: 08fdef76-d0ed-4d7f-ac0f-e95b46d14458
ORCID for E. Ntotsios: ORCID iD orcid.org/0000-0001-7382-0948

Catalogue record

Date deposited: 26 Apr 2019 16:30
Last modified: 23 Feb 2023 03:01

Export record

Contributors

Author: H. F. Lam
Author: C. Papadimitriou
Author: E. Ntotsios ORCID iD
Editor: B.F. Spencer
Editor: M. Tomizuka
Editor: C.B. Yun
Editor: W.M. Chen
Editor: R.W. Chen

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.

×