Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification
The big data collected from structural health monitoring systems (SHMs), combined with the rapid advances in machine learning (ML), have enabled data-driven methods in practical SHM applications. These methods typically use ML algorithms to identify patterns within features extracted from data repre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/6/145 |
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| author | Long Li Xiaoming Tao Hui Song Xiaolong Li Zhilong Ye Yao Jin Qiuyu He Shiyin Wei Wenli Chen |
| author_facet | Long Li Xiaoming Tao Hui Song Xiaolong Li Zhilong Ye Yao Jin Qiuyu He Shiyin Wei Wenli Chen |
| author_sort | Long Li |
| collection | DOAJ |
| description | The big data collected from structural health monitoring systems (SHMs), combined with the rapid advances in machine learning (ML), have enabled data-driven methods in practical SHM applications. These methods typically use ML algorithms to identify patterns within features extracted from data representing structural conditions, thereby inferring damage from changes in these patterns. However, data-driven models often struggle to generalize effectively to unseen datasets. This study addresses this challenge through three key contributions: dataset augmentation, an efficient feature representation, and a probabilistic modeling approach. First, a data augmentation method leveraging the symmetric properties of bridge structures is introduced to enhance dataset diversity. Second, a novel damage indicator named Fre-GraRMSC1 is proposed, capable of distinguishing both damage locations and severity. Finally, a probabilistic generative model based on a deep belief network (DBN) is developed to predict damage locations and degrees. The proposed methods are validated using vibration data from a numerical three-span continuous bridge subjected to random vehicle excitations. Results demonstrate high accuracy in damage identification and improved generalization performance. |
| format | Article |
| id | doaj-art-7457003b5471491e9c680eb97bc8ed62 |
| institution | Kabale University |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-7457003b5471491e9c680eb97bc8ed622025-08-20T03:27:29ZengMDPI AGInfrastructures2412-38112025-06-0110614510.3390/infrastructures10060145Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage IdentificationLong Li0Xiaoming Tao1Hui Song2Xiaolong Li3Zhilong Ye4Yao Jin5Qiuyu He6Shiyin Wei7Wenli Chen8CCCC Highway Consultants Co., Ltd., Beijing 100088, ChinaSchool of Civil Engineering, Harbin Institute of Technology, Harbin 150090, ChinaCCCC Highway Consultants Co., Ltd., Beijing 100088, ChinaCCCC Highway Consultants Co., Ltd., Beijing 100088, ChinaCCCC Highway Consultants Co., Ltd., Beijing 100088, ChinaCCCC Highway Consultants Co., Ltd., Beijing 100088, ChinaCCCC Highway Consultants Co., Ltd., Beijing 100088, ChinaSchool of Civil Engineering, Harbin Institute of Technology, Harbin 150090, ChinaSchool of Civil Engineering, Harbin Institute of Technology, Harbin 150090, ChinaThe big data collected from structural health monitoring systems (SHMs), combined with the rapid advances in machine learning (ML), have enabled data-driven methods in practical SHM applications. These methods typically use ML algorithms to identify patterns within features extracted from data representing structural conditions, thereby inferring damage from changes in these patterns. However, data-driven models often struggle to generalize effectively to unseen datasets. This study addresses this challenge through three key contributions: dataset augmentation, an efficient feature representation, and a probabilistic modeling approach. First, a data augmentation method leveraging the symmetric properties of bridge structures is introduced to enhance dataset diversity. Second, a novel damage indicator named Fre-GraRMSC1 is proposed, capable of distinguishing both damage locations and severity. Finally, a probabilistic generative model based on a deep belief network (DBN) is developed to predict damage locations and degrees. The proposed methods are validated using vibration data from a numerical three-span continuous bridge subjected to random vehicle excitations. Results demonstrate high accuracy in damage identification and improved generalization performance.https://www.mdpi.com/2412-3811/10/6/145structural health monitoringdamage identificationdeep learningdata augmentationdeep belief network |
| spellingShingle | Long Li Xiaoming Tao Hui Song Xiaolong Li Zhilong Ye Yao Jin Qiuyu He Shiyin Wei Wenli Chen Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification Infrastructures structural health monitoring damage identification deep learning data augmentation deep belief network |
| title | Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification |
| title_full | Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification |
| title_fullStr | Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification |
| title_full_unstemmed | Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification |
| title_short | Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification |
| title_sort | symmetry based data augmentation method for deep learning based structural damage identification |
| topic | structural health monitoring damage identification deep learning data augmentation deep belief network |
| url | https://www.mdpi.com/2412-3811/10/6/145 |
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