Damage identification of steel-concrete composite beams based on modal strain energy changes through general regression neural network

Sadeghi, F; Yu, Y; Zhu, X; Li, J

Damage identification of steel-concrete composite beams based on modal strain energy changes through general regression neural network

Sadeghi, F Yu, Y

Zhu, X

Li, J

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Engineering Structures, 2021, 244
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Sadeghi, F
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Zhu, X https://orcid.org/0000-0001-5083-9320
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048
dc.date.accessioned	2022-04-11T03:58:49Z
dc.date.available	2022-04-11T03:58:49Z
dc.date.issued	2021-10-01
dc.identifier.citation	Engineering Structures, 2021, 244
dc.identifier.issn	0141-0296
dc.identifier.issn	1873-7323
dc.identifier.uri	http://hdl.handle.net/10453/156049
dc.description.abstract	This paper presents a novel method for damage identification of steel-concrete composite beams based on modal strain energy (MSE) changes through general regression neural network (GRNN). A finite element (FE) model was developed using two Euler-Bernoulli beam elements as steel beam and concrete slab layers which are coupled by incorporating a deformable shear connection layer distributed at their interface. The connection layer was modelled as a uniform spring that enables both longitudinal slip and vertical uplift between the two components. The FE model was validated using experimental results of a full-scale composite beam in the laboratory. Three damage indices were defined as the elemental stiffness reduction in the steel, concrete and shear connection layers, respectively. A damage identification approach was developed to investigate the sensitivity of eigenvalues to damage in the composite interface. Then, MSEs change ratios were selected as the features for identifying structural damage in the composite layers. Principle component analysis was utilised to reduce the dimensionality of the large data features obtained from modal analysis leading to determining the main features for structural damage identification. The low dimensional data were employed as the input for the GRNN. Different damage cases were investigated, and the damage vectors were defined as the outputs of GRNN. The results show that the proposed method is efficient and reliable to identify damage in the composite beam with a few low vibration modes, even though small damage in the composite layers does not significantly affect these modes.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Engineering Structures
dc.relation.isbasedon	10.1016/j.engstruct.2021.112824
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0912 Materials Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Civil Engineering
dc.title	Damage identification of steel-concrete composite beams based on modal strain energy changes through general regression neural network
dc.type	Journal Article
utslib.citation.volume	244
utslib.for	0905 Civil Engineering
utslib.for	0912 Materials Engineering
utslib.for	0915 Interdisciplinary Engineering
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	*
dc.date.updated	2022-04-11T03:58:46Z
pubs.publication-status	Published
pubs.volume	244

Abstract:

This paper presents a novel method for damage identification of steel-concrete composite beams based on modal strain energy (MSE) changes through general regression neural network (GRNN). A finite element (FE) model was developed using two Euler-Bernoulli beam elements as steel beam and concrete slab layers which are coupled by incorporating a deformable shear connection layer distributed at their interface. The connection layer was modelled as a uniform spring that enables both longitudinal slip and vertical uplift between the two components. The FE model was validated using experimental results of a full-scale composite beam in the laboratory. Three damage indices were defined as the elemental stiffness reduction in the steel, concrete and shear connection layers, respectively. A damage identification approach was developed to investigate the sensitivity of eigenvalues to damage in the composite interface. Then, MSEs change ratios were selected as the features for identifying structural damage in the composite layers. Principle component analysis was utilised to reduce the dimensionality of the large data features obtained from modal analysis leading to determining the main features for structural damage identification. The low dimensional data were employed as the input for the GRNN. Different damage cases were investigated, and the damage vectors were defined as the outputs of GRNN. The results show that the proposed method is efficient and reliable to identify damage in the composite beam with a few low vibration modes, even though small damage in the composite layers does not significantly affect these modes.

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http://hdl.handle.net/10453/156049