Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study

Noori Hoshyar, A; Rashidi, M; Yu, Y; Samali, B

Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study

Noori Hoshyar, A Rashidi, M Yu, Y

Samali, B

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Remote Sensing, 2023, 15, (8)
Issue Date:: 2023-04-01

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Field	Value	Language
dc.contributor.author	Noori Hoshyar, A
dc.contributor.author	Rashidi, M
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Samali, B
dc.date.accessioned	2024-01-18T22:58:44Z
dc.date.available	2024-01-18T22:58:44Z
dc.date.issued	2023-04-01
dc.identifier.citation	Remote Sensing, 2023, 15, (8)
dc.identifier.issn	2072-4292
dc.identifier.issn	2072-4292
dc.identifier.uri	http://hdl.handle.net/10453/174780
dc.description.abstract	Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Remote Sensing
dc.relation.isbasedon	10.3390/rs15081984
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0203 Classical Physics, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.subject.classification	3701 Atmospheric sciences
dc.subject.classification	3709 Physical geography and environmental geoscience
dc.subject.classification	4013 Geomatic engineering
dc.title	Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0203 Classical Physics
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0909 Geomatic 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	2024-01-18T22:58:42Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	8

Abstract:

Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms.

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http://hdl.handle.net/10453/174780