A hybrid domain adaptation approach for estimation of prestressed forces in prestressed concrete bridges under moving vehicles

Talaei, S; Zhu, X; Li, J; Yu, Y; Chan, THT

A hybrid domain adaptation approach for estimation of prestressed forces in prestressed concrete bridges under moving vehicles

Talaei, S

Zhu, X

Li, J

Yu, Y Chan, THT

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Engineering Structures, 2025, 330
Issue Date:: 2025-05-01

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Field	Value	Language
dc.contributor.author	Talaei, S https://orcid.org/0000-0001-6400-4367
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.contributor.author	Yu, Y
dc.contributor.author	Chan, THT
dc.date.accessioned	2025-04-14T00:05:11Z
dc.date.available	2025-04-14T00:05:11Z
dc.date.issued	2025-05-01
dc.identifier.citation	Engineering Structures, 2025, 330
dc.identifier.issn	0141-0296
dc.identifier.issn	1873-7323
dc.identifier.uri	http://hdl.handle.net/10453/186866
dc.description.abstract	Ensuring the accurate and reliable estimation of prestressed forces (PFs) in prestressed concrete bridges is vital for operational performance and public safety. Traditional vibration-based methods often face challenges when applied to real-world scenarios. In this study, a novel hybrid domain adaptation approach is proposed to predict the prestressed force of prestressed concrete bridges under moving vehicles. The finite element model for Vehicle-Bridge Interaction (VBI) systems is established and validated using the experimental results. This validated model is then used to generate a synthetic training dataset of the source domain. Continuous Wavelet Transform (CWT) is employed for feature extraction from VBI acceleration responses, capturing their time-frequency properties. Preliminary feature extraction is enhanced through the use of a pre-trained AlexNet network. Following this initial step, a novel hybrid domain adaptation (DA) approach is applied to close the gap between the synthetic and real-world data. Specifically, Maximum Mean Discrepancy (MMD) and adversarial DA techniques are synergistically combined. Three types of loss functions are incorporated: Regression Loss for precise force prediction, MMD Loss to align the synthetic and real-world data domains, and Adversarial Loss to ensure domain invariance. The effects of uncertainties in VBI system, such as errors of the bridge length, flexural rigidity, density, damping, boundary conditions, errors of the vehicle model and moving speed and road surface roughness, have been discussed. The results indicate that the integrated approach effectively mitigates the challenges posed by domain shift, enabling robust and reliable predictions of PFs in actual bridge structures. The finding of this study offers a comprehensive, data-driven solution with significant implications for the future of structural health monitoring and bridge condition assessment.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation	http://purl.org/au-research/grants/arc/DP220102045
dc.relation.ispartof	Engineering Structures
dc.relation.isbasedon	10.1016/j.engstruct.2025.119904
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 0912 Materials Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Civil Engineering
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4016 Materials engineering
dc.title	A hybrid domain adaptation approach for estimation of prestressed forces in prestressed concrete bridges under moving vehicles
dc.type	Journal Article
utslib.citation.volume	330
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/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	2025-04-14T00:05:09Z
pubs.publication-status	Accepted
pubs.volume	330

Abstract:

Ensuring the accurate and reliable estimation of prestressed forces (PFs) in prestressed concrete bridges is vital for operational performance and public safety. Traditional vibration-based methods often face challenges when applied to real-world scenarios. In this study, a novel hybrid domain adaptation approach is proposed to predict the prestressed force of prestressed concrete bridges under moving vehicles. The finite element model for Vehicle-Bridge Interaction (VBI) systems is established and validated using the experimental results. This validated model is then used to generate a synthetic training dataset of the source domain. Continuous Wavelet Transform (CWT) is employed for feature extraction from VBI acceleration responses, capturing their time-frequency properties. Preliminary feature extraction is enhanced through the use of a pre-trained AlexNet network. Following this initial step, a novel hybrid domain adaptation (DA) approach is applied to close the gap between the synthetic and real-world data. Specifically, Maximum Mean Discrepancy (MMD) and adversarial DA techniques are synergistically combined. Three types of loss functions are incorporated: Regression Loss for precise force prediction, MMD Loss to align the synthetic and real-world data domains, and Adversarial Loss to ensure domain invariance. The effects of uncertainties in VBI system, such as errors of the bridge length, flexural rigidity, density, damping, boundary conditions, errors of the vehicle model and moving speed and road surface roughness, have been discussed. The results indicate that the integrated approach effectively mitigates the challenges posed by domain shift, enabling robust and reliable predictions of PFs in actual bridge structures. The finding of this study offers a comprehensive, data-driven solution with significant implications for the future of structural health monitoring and bridge condition assessment.

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http://hdl.handle.net/10453/186866