Enhanced damage detection for noisy input signals using improved reptile search algorithm and data analytics techniques

Hassani, S; Dackermann, U; Mousavi, M; Li, J

Enhanced damage detection for noisy input signals using improved reptile search algorithm and data analytics techniques

Hassani, S Dackermann, U Mousavi, M Li, J

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Computers and Structures, 2024, 296, pp. 107293
Issue Date:: 2024-06-01

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Field	Value	Language
dc.contributor.author	Hassani, S
dc.contributor.author	Dackermann, U
dc.contributor.author	Mousavi, M
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048
dc.date.accessioned	2025-04-03T06:22:05Z
dc.date.available	2025-04-03T06:22:05Z
dc.date.issued	2024-06-01
dc.identifier.citation	Computers and Structures, 2024, 296, pp. 107293
dc.identifier.issn	0045-7949
dc.identifier.uri	http://hdl.handle.net/10453/186565
dc.description.abstract	The sensitivity of structural health monitoring systems to environmental and operational conditions poses a significant challenge due to their inherent susceptibility to outliers. This paper proposes an effective model-updating-based optimization algorithm that can alleviate the impact of outliers associated with field and operational fluctuations. The proposed method addresses the influence of uncertainties from sources such as white noise, colored noise, and measurement errors, which can introduce outliers in datasets. The approach comprises a hybrid procedure in which a Gaussian smoothing technique is first employed to smooth out measured data to reduce the impact of irregularities. Next, Johansen cointegration is employed for raw data fusion to further enhance the signature of shared patterns. A novel optimization algorithm based on the Reptile Search Algorithm (RSA), named Improved RSA (IRSA), is proposed to solve the objective function based on the concept of mutual information. This algorithm provides a superior solution with much improved computational speed and accuracy compared to RSA. The new hybrid method was validated by several numerical and experimental damage detection studies. Furthermore, it was compared to other state-of-the-art methods described in the literature. The results clearly demonstrate the superior performance of the newly developed method.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Computers and Structures
dc.relation.isbasedon	10.1016/j.compstruc.2024.107293
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	09 Engineering
dc.subject.classification	Applied Mathematics
dc.subject.classification	40 Engineering
dc.title	Enhanced damage detection for noisy input signals using improved reptile search algorithm and data analytics techniques
dc.type	Journal Article
utslib.citation.volume	296
utslib.for	09 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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Built Infrastructure Resilience (CBIR)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-04-03T06:22:03Z
pubs.publication-status	Published
pubs.volume	296

Abstract:

The sensitivity of structural health monitoring systems to environmental and operational conditions poses a significant challenge due to their inherent susceptibility to outliers. This paper proposes an effective model-updating-based optimization algorithm that can alleviate the impact of outliers associated with field and operational fluctuations. The proposed method addresses the influence of uncertainties from sources such as white noise, colored noise, and measurement errors, which can introduce outliers in datasets. The approach comprises a hybrid procedure in which a Gaussian smoothing technique is first employed to smooth out measured data to reduce the impact of irregularities. Next, Johansen cointegration is employed for raw data fusion to further enhance the signature of shared patterns. A novel optimization algorithm based on the Reptile Search Algorithm (RSA), named Improved RSA (IRSA), is proposed to solve the objective function based on the concept of mutual information. This algorithm provides a superior solution with much improved computational speed and accuracy compared to RSA. The new hybrid method was validated by several numerical and experimental damage detection studies. Furthermore, it was compared to other state-of-the-art methods described in the literature. The results clearly demonstrate the superior performance of the newly developed method.

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http://hdl.handle.net/10453/186565