Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder
Structural health monitoring (SHM) is a hot research topic with the main purpose of damage detection in a structure and assessing its health state. The major focus of SHM studies in recent years has been on developing vibration-based damage detection algorithms and using machine learning, especially...
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| Vydáno v: | Shock and vibration Ročník 2021; číslo 1 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Cairo
Hindawi
2021
John Wiley & Sons, Inc Wiley |
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| ISSN: | 1070-9622, 1875-9203 |
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| Abstract | Structural health monitoring (SHM) is a hot research topic with the main purpose of damage detection in a structure and assessing its health state. The major focus of SHM studies in recent years has been on developing vibration-based damage detection algorithms and using machine learning, especially deep learning-based approaches. Most of the deep learning-based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. As it is not usually possible to collect data from damaged states of a large civil structure, using such algorithms for these structures may be impractical. This paper proposes a new unsupervised deep learning-based method for structural damage detection based on convolutional autoencoders (CAEs). The main objective of the proposed method is to identify and quantify structural damage using a CAE network that employs raw vibration signals from the structure and is trained by the signals solely acquired from the healthy state of the structure. The CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase. Applications on the two numerical models of IASC-ASCE benchmark structure and a grid structure located at the University of Central Florida, as well as the full-scale Tianjin Yonghe Bridge, prove the efficiency of the proposed algorithm in assessing the global health state of the structures and quantifying the damage. |
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| AbstractList | Structural health monitoring (SHM) is a hot research topic with the main purpose of damage detection in a structure and assessing its health state. The major focus of SHM studies in recent years has been on developing vibration‐based damage detection algorithms and using machine learning, especially deep learning‐based approaches. Most of the deep learning‐based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. As it is not usually possible to collect data from damaged states of a large civil structure, using such algorithms for these structures may be impractical. This paper proposes a new unsupervised deep learning‐based method for structural damage detection based on convolutional autoencoders (CAEs). The main objective of the proposed method is to identify and quantify structural damage using a CAE network that employs raw vibration signals from the structure and is trained by the signals solely acquired from the healthy state of the structure. The CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase. Applications on the two numerical models of IASC‐ASCE benchmark structure and a grid structure located at the University of Central Florida, as well as the full‐scale Tianjin Yonghe Bridge, prove the efficiency of the proposed algorithm in assessing the global health state of the structures and quantifying the damage. |
| Audience | Academic |
| Author | Rastin, Zahra Ghodrati Amiri, Gholamreza Darvishan, Ehsan |
| Author_xml | – sequence: 1 givenname: Zahra orcidid: 0000-0001-9018-3960 surname: Rastin fullname: Rastin, Zahra organization: Natural Disasters Prevention Research CenterSchool of Civil EngineeringIran University of Science & TechnologyTehranIraniust.ac.ir – sequence: 2 givenname: Gholamreza orcidid: 0000-0003-3244-0943 surname: Ghodrati Amiri fullname: Ghodrati Amiri, Gholamreza organization: Natural Disasters Prevention Research CenterSchool of Civil EngineeringIran University of Science & TechnologyTehranIraniust.ac.ir – sequence: 3 givenname: Ehsan orcidid: 0000-0003-2010-8919 surname: Darvishan fullname: Darvishan, Ehsan organization: Department of Civil EngineeringRoudehen BranchIslamic Azad UniversityRoudehenIranazad.ac.ir |
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| Copyright | Copyright © 2021 Zahra Rastin et al. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Zahra Rastin et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Algorithms Analysis Concrete Convolution Damage detection Data collection Deep learning Fault diagnosis Feature extraction Fuzzy logic Localization Machine learning Methods Neural networks Numerical models Prestressed concrete Principal components analysis Public health Structural damage Structural health monitoring Support vector machines Training Vibration Wavelet transforms |
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| Title | Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder |
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