Structural condition assessment using entropy-based time series analysis

Makki Alamdari, M; Samali, B; Li, J; Lu, Y; Mustapha, S

Structural condition assessment using entropy-based time series analysis

Makki Alamdari, M Samali, B

Li, J

Lu, Y Mustapha, S

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Publication Type:: Journal Article
Citation:: Journal of Intelligent Material Systems and Structures, 2017, 28 (14), pp. 1941 - 1956
Issue Date:: 2017-08-01

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Field	Value	Language
dc.contributor.author	Makki Alamdari, M	en_US
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.contributor.author	Lu, Y	en_US
dc.contributor.author	Mustapha, S	en_US
dc.date.issued	2017-08-01	en_US
dc.identifier.citation	Journal of Intelligent Material Systems and Structures, 2017, 28 (14), pp. 1941 - 1956	en_US
dc.identifier.issn	1045-389X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125112
dc.description.abstract	© 2017, © The Author(s) 2017. We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches; hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes; this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.	en_US
dc.relation.ispartof	Journal of Intelligent Material Systems and Structures	en_US
dc.relation.isbasedon	10.1177/1045389X16679288	en_US
dc.subject.classification	Materials	en_US
dc.title	Structural condition assessment using entropy-based time series analysis	en_US
dc.type	Journal Article
utslib.citation.volume	14	en_US
utslib.citation.volume	28	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.issue	14	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

© 2017, © The Author(s) 2017. We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches; hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes; this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.

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http://hdl.handle.net/10453/125112