Unsupervised learning-based framework for indirect structural health monitoring using adversarial autoencoder

Calderon Hurtado, A.; Kaur, K.; Makki Alamdari, M.; Atroshchenko, E.; Chang, K.C.; Kim, C.W.

doi:10.1016/j.jsv.2023.117598

Cited by 56Structural Health Monitoring TechniquesInfrastructure Maintenance and MonitoringStructural Engineering and Materials Analysis

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Unsupervised learning-based framework for indirect structural health monitoring using adversarial autoencoder

Calderon Hurtado, A., Kaur, K., Makki Alamdari, M., Atroshchenko, E., Chang, K.C., Kim, C.W.·2023·Journal of Sound and Vibration

DOI 10.1016/j.jsv.2023.117598

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DL has been applied to SHM to identify, locate, and evaluate various damages by extracting advanced damage features from data such as pictures and vibration signals.
“In addition, DL has also been applied to SHM to identify, locate, and evaluate various damages by extracting advanced damage features from data such as pictures and vibration signals [15,35,36].”
From Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends (2023) · cites this paper as [15]
Currently, the most commonly used SHM methods are vibration-based SHM, AE-based SHM, GW-based SHM, and EMI-based SHM.
“Currently, the most commonly used SHM methods are vibration-based SHM [14,15,16,17,18], AE-based SHM [9,19,20], Guided Wave (GW) based SHM [21,22,23,24,25], and Electro-Mechanical Impedance (EMI) based SHM [26,27].”
From Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends (2023) · cites this paper as [15]
Generally, the training set's ratio to the test set is 8:2 or 7:3.
“Generally, the training set’s ratio to the test set is 8:2 [27,36,182,198] or 7:3 [15,58,187].”
From Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends (2023) · cites this paper as [15]

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Damage Detection and Localization for Indirect Bridge Monitoring Exploiting Adversarial Autoencoder and Wavelet Transform
Kaur, K., Alamdari, M. M., Chang, K. C., Kim, C. W., & Atroshchenko, E. (2022) · Lecture notes in civil engineering · doi:10.1007/978-3-031-07254-3_67 · Cited by 8
Self-citation
An Unsupervised Learning Method for Indirect Bridge Structural Health Monitoring
Hurtado, A. C., Alamdari, M. M., Atroshchenko, E., Chang, K. C., & Kim, C. (2023) · Lecture notes in civil engineering · doi:10.1007/978-3-031-39117-0_13 · Cited by 4
Self-citation
Autoencoders for unsupervised real-time bridge health assessment
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Authors: Calderon Hurtado, A., Kaur, K., Makki Alamdari, M., Atroshchenko, E., Chang, K. C., & Kim, C. W.
Affiliations: UNSW Sydney (AU)
Kyoto University (JP)
ORCID: Calderon Hurtado, A.
Kaur, K.
Makki Alamdari, M.
Atroshchenko, E.
Chang, K.C.
Kim, C.W.
Venue: Journal of Sound and Vibration
Publisher: Elsevier BV
Published: 2023-04
Volume: 550
Pages: 117598
ISSN: 0022-460X (ISSN-L)
DOI: 10.1016/j.jsv.2023.117598
OpenAlex: W4319460153
Cited by: 53
References: 69
Citations by year: 2026: 17 · 2025: 13 · 2024: 18 · 2023: 5
FWCI: 6.21
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Journal of Sound and Vibration (publishedVersion)
Funders: Japan Society for the Promotion of Science
Australian Research Council (DE210101625)
Topics: Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
Keywords: adversarial autoencoder, unsupervised damage detection, bridge health monitoring, vehicle-bridge interaction, damage severity estimation, reconstruction error, frequency filtering, signal averaging, simply supported beams, half-car model, bridge condition monitoring

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Unsupervised learning-based framework for indirect structural health monitoring using adversarial autoencoder

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