Monitoring onsite-temperature prediction error for condition monitoring of civil infrastructures

Mousavi, M; Gandomi, AH; Abdel Wahab, M; Glisic, B

Monitoring onsite-temperature prediction error for condition monitoring of civil infrastructures

Mousavi, M

Gandomi, AH Abdel Wahab, M Glisic, B

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Publisher:: Hindawi
Publication Type:: Journal Article
Citation:: Structural Control and Health Monitoring, 2022, 29, (12)
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Mousavi, M https://orcid.org/0000-0002-0436-5176
dc.contributor.author	Gandomi, AH
dc.contributor.author	Abdel Wahab, M
dc.contributor.author	Glisic, B
dc.date.accessioned	2023-01-19T08:34:29Z
dc.date.available	2023-01-19T08:34:29Z
dc.date.issued	2022-12-01
dc.identifier.citation	Structural Control and Health Monitoring, 2022, 29, (12)
dc.identifier.issn	1545-2255
dc.identifier.issn	1545-2263
dc.identifier.uri	http://hdl.handle.net/10453/165260
dc.description.abstract	An inverse input–output method is proposed for long-term condition monitoring of civil infrastructures through monitoring the prediction error of air temperature recorded at the site of a structure. It is known that structural natural frequencies are affected by temperature. Hence, the proposed method considers the structural natural frequencies as input and temperature as output to train a machine learning algorithm (MLA). To this end, after signal preprocessing using the variational mode decomposition (VMD), different MLAs are employed, and the error associated with this prediction is regarded as damage–sensitive feature. It is hypothesised and further confirmed through solving numerical and benchmark problems that the prediction error deviates significantly from the upper bond control limit of an R-chart (errors signal) constructed based on the prediction error of temperature as soon as the damage occurs. The frequency–temperature scatter plots indicate a linear dependency between the natural frequencies and temperature. Moreover, the similar slope obtained for the regression line fitted to different frequency–temperature scatter plots indicates high collinearity among pairs of natural frequencies. This observation implies that an interaction term must be considered for such pairs of natural frequencies in the linear regression model. The results of both numerical and experimental studies further confirm that the interaction linear regression model is the most accurate machine learning algorithm for solving the inverse problem of predicting temperature using natural frequencies for condition monitoring of structures. The results of the proposed method are also compared with the direct strategy, whereby its superiority is demonstrated.
dc.language	en
dc.publisher	Hindawi
dc.relation.ispartof	Structural Control and Health Monitoring
dc.relation.isbasedon	10.1002/stc.3112
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering
dc.subject.classification	Civil Engineering
dc.title	Monitoring onsite-temperature prediction error for condition monitoring of civil infrastructures
dc.type	Journal Article
utslib.citation.volume	29
utslib.for	0905 Civil Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2023-01-19T08:34:28Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	29
utslib.citation.issue	12

Abstract:

An inverse input–output method is proposed for long-term condition monitoring of civil infrastructures through monitoring the prediction error of air temperature recorded at the site of a structure. It is known that structural natural frequencies are affected by temperature. Hence, the proposed method considers the structural natural frequencies as input and temperature as output to train a machine learning algorithm (MLA). To this end, after signal preprocessing using the variational mode decomposition (VMD), different MLAs are employed, and the error associated with this prediction is regarded as damage–sensitive feature. It is hypothesised and further confirmed through solving numerical and benchmark problems that the prediction error deviates significantly from the upper bond control limit of an R-chart (errors signal) constructed based on the prediction error of temperature as soon as the damage occurs. The frequency–temperature scatter plots indicate a linear dependency between the natural frequencies and temperature. Moreover, the similar slope obtained for the regression line fitted to different frequency–temperature scatter plots indicates high collinearity among pairs of natural frequencies. This observation implies that an interaction term must be considered for such pairs of natural frequencies in the linear regression model. The results of both numerical and experimental studies further confirm that the interaction linear regression model is the most accurate machine learning algorithm for solving the inverse problem of predicting temperature using natural frequencies for condition monitoring of structures. The results of the proposed method are also compared with the direct strategy, whereby its superiority is demonstrated.

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http://hdl.handle.net/10453/165260