Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network
The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural ne...
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| Format: | Article |
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MDPI AG
2021-06-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/14/6/180 |
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| author | Lei Fu Qizhi Tang Peng Gao Jingzhou Xin Jianting Zhou |
| author_facet | Lei Fu Qizhi Tang Peng Gao Jingzhou Xin Jianting Zhou |
| author_sort | Lei Fu |
| collection | DOAJ |
| description | The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation. |
| first_indexed | 2024-03-10T10:38:27Z |
| format | Article |
| id | doaj.art-d03c4f3dc68944ff888210c4ae2946dd |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T10:38:27Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-d03c4f3dc68944ff888210c4ae2946dd2023-11-21T23:10:43ZengMDPI AGAlgorithms1999-48932021-06-0114618010.3390/a14060180Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory NetworkLei Fu0Qizhi Tang1Peng Gao2Jingzhou Xin3Jianting Zhou4State Key Laboratory of Mountain Bridges and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaState Key Laboratory of Mountain Bridges and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaCMCU Engineering Co., Ltd., Chongqing 400039, ChinaState Key Laboratory of Mountain Bridges and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaState Key Laboratory of Mountain Bridges and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaThe shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.https://www.mdpi.com/1999-4893/14/6/180long-span bridgelong short-term memory networkconvolutional neural networkdamage identification |
| spellingShingle | Lei Fu Qizhi Tang Peng Gao Jingzhou Xin Jianting Zhou Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network Algorithms long-span bridge long short-term memory network convolutional neural network damage identification |
| title | Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network |
| title_full | Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network |
| title_fullStr | Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network |
| title_full_unstemmed | Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network |
| title_short | Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network |
| title_sort | damage identification of long span bridges using the hybrid of convolutional neural network and long short term memory network |
| topic | long-span bridge long short-term memory network convolutional neural network damage identification |
| url | https://www.mdpi.com/1999-4893/14/6/180 |
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