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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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6658575 |
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| author | Zahra Rastin Gholamreza Ghodrati Amiri Ehsan Darvishan |
| author_facet | Zahra Rastin Gholamreza Ghodrati Amiri Ehsan Darvishan |
| author_sort | Zahra Rastin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-85bfceeec25044dca7aca77a7b535b3c |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-85bfceeec25044dca7aca77a7b535b3c2025-08-20T03:37:46ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66585756658575Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional AutoencoderZahra Rastin0Gholamreza Ghodrati Amiri1Ehsan Darvishan2Natural Disasters Prevention Research Center, School of Civil Engineering, Iran University of Science & Technology, Tehran, IranNatural Disasters Prevention Research Center, School of Civil Engineering, Iran University of Science & Technology, Tehran, IranDepartment of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, IranStructural 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.http://dx.doi.org/10.1155/2021/6658575 |
| spellingShingle | Zahra Rastin Gholamreza Ghodrati Amiri Ehsan Darvishan Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder Shock and Vibration |
| title | Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder |
| title_full | Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder |
| title_fullStr | Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder |
| title_full_unstemmed | Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder |
| title_short | Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder |
| title_sort | unsupervised structural damage detection technique based on a deep convolutional autoencoder |
| url | http://dx.doi.org/10.1155/2021/6658575 |
| work_keys_str_mv | AT zahrarastin unsupervisedstructuraldamagedetectiontechniquebasedonadeepconvolutionalautoencoder AT gholamrezaghodratiamiri unsupervisedstructuraldamagedetectiontechniquebasedonadeepconvolutionalautoencoder AT ehsandarvishan unsupervisedstructuraldamagedetectiontechniquebasedonadeepconvolutionalautoencoder |