A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning
Rapid and accurate identification of moving load is crucial for bridge operation management and early warning of overload events. However, it is hard to obtain them rapidly via traditional machine learning methods, due to their massive model parameters and complex network structure. To this end, thi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/2/572 |
| _version_ | 1827758155778490368 |
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| author | Yilun Qin Qizhi Tang Jingzhou Xin Changxi Yang Zixiang Zhang Xianyi Yang |
| author_facet | Yilun Qin Qizhi Tang Jingzhou Xin Changxi Yang Zixiang Zhang Xianyi Yang |
| author_sort | Yilun Qin |
| collection | DOAJ |
| description | Rapid and accurate identification of moving load is crucial for bridge operation management and early warning of overload events. However, it is hard to obtain them rapidly via traditional machine learning methods, due to their massive model parameters and complex network structure. To this end, this paper proposes a novel method to perform moving loads identification using MobileNetV2 and transfer learning. Specifically, the dynamic responses of a vehicle–bridge interaction system are firstly transformed into a two-dimensional time-frequency image by continuous wavelet transform to construct the database. Secondly, a pre-trained MobileNetV2 model based on ImageNet is transferred to the moving load identification task by transfer learning strategy for describing the mapping relationship between structural response and these specified moving loads. Then, load identification can be performed through inputting bridge responses into the established relationship. Finally, the effectiveness of the method is verified by numerical simulation. The results show that it can accurately identify the vehicle weight, vehicle speed information, and presents excellent strong robustness. In addition, MobileNetV2 has faster identification speed and requires less computational resources than several traditional deep convolutional neural network models in moving load identification, which can provide a novel idea for the rapid identification of moving loads. |
| first_indexed | 2024-03-11T09:02:42Z |
| format | Article |
| id | doaj.art-1edff6ace2dc43f4a69c1878dabae3e9 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T09:02:42Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-1edff6ace2dc43f4a69c1878dabae3e92023-11-16T19:34:38ZengMDPI AGBuildings2075-53092023-02-0113257210.3390/buildings13020572A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer LearningYilun Qin0Qizhi Tang1Jingzhou Xin2Changxi Yang3Zixiang Zhang4Xianyi Yang5State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaState Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaState Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaState Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaCCCC Second Highway Consultants Co., Ltd., Wuhan 430058, ChinaState Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaRapid and accurate identification of moving load is crucial for bridge operation management and early warning of overload events. However, it is hard to obtain them rapidly via traditional machine learning methods, due to their massive model parameters and complex network structure. To this end, this paper proposes a novel method to perform moving loads identification using MobileNetV2 and transfer learning. Specifically, the dynamic responses of a vehicle–bridge interaction system are firstly transformed into a two-dimensional time-frequency image by continuous wavelet transform to construct the database. Secondly, a pre-trained MobileNetV2 model based on ImageNet is transferred to the moving load identification task by transfer learning strategy for describing the mapping relationship between structural response and these specified moving loads. Then, load identification can be performed through inputting bridge responses into the established relationship. Finally, the effectiveness of the method is verified by numerical simulation. The results show that it can accurately identify the vehicle weight, vehicle speed information, and presents excellent strong robustness. In addition, MobileNetV2 has faster identification speed and requires less computational resources than several traditional deep convolutional neural network models in moving load identification, which can provide a novel idea for the rapid identification of moving loads.https://www.mdpi.com/2075-5309/13/2/572bridge engineeringmoving loads identificationMobileNetV2transfer learning |
| spellingShingle | Yilun Qin Qizhi Tang Jingzhou Xin Changxi Yang Zixiang Zhang Xianyi Yang A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning Buildings bridge engineering moving loads identification MobileNetV2 transfer learning |
| title | A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning |
| title_full | A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning |
| title_fullStr | A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning |
| title_full_unstemmed | A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning |
| title_short | A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning |
| title_sort | rapid identification technique of moving loads based on mobilenetv2 and transfer learning |
| topic | bridge engineering moving loads identification MobileNetV2 transfer learning |
| url | https://www.mdpi.com/2075-5309/13/2/572 |
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