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Structural damage identification based on graph convolutional neural network under strong noise and small sample conditions
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Xing LI1, Yongpeng LUO1, 2, 3, Xu GUO1, Feiyu LIAO1, 3, Siping LU4
Earthquake Engineering and Engineering Dynamics | 2024, 44(3) : 52 - 60
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Earthquake Engineering and Engineering Dynamics | 2024, 44(3): 52-60
Structural damage identification based on graph convolutional neural network under strong noise and small sample conditions
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Xing LI1, Yongpeng LUO1, 2, 3, Xu GUO1, Feiyu LIAO1, 3, Siping LU4
Affiliations
  • 1.School of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
  • 2.Key Laboratory for Structural Engineering and Disaster Prevention of Fujian Province (Huaqiao University), Xiamen 361021, China
  • 3.Digital Fujian Intelligent Transportation Technology Internet of Things Laboratory, Fuzhou 350108, China
  • 4.School of Civil Engineering, Central South University, Changsha 410075, China
doi: 10.13197/j.eeed.2024.0305
Outline
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Structural damage identification based on deep learning are mainly realized by capturing the characteristics and internal rules of data. Insufficient training samples and noise interference may lead to the failure of mining effective features and internal laws. It is particularly important to mine information as much as possible from the data for damage identification. To solve these problems, a structural damage identification method based on graph convolutional network (GCN) is proposed. In order to extract more features, considering the correlation between different position sensors and the characteristics of each sensor data, one-dimensional vibration data was converted into graph data by the graph construction method. Subsequently, GCN was used to extract the data features of the graph samples and achieve rapid classification to achieve the purpose of damage identification. The feasibility and reliability of the proposed method were verified by the Qatar University grandstand simulator structure, and the effects of noise level, number of samples, the method of graph construction and convolutional network parameters on the recognition results were discussed. The results show that, compared with 1 dimensional convolutional neural network, the GCN model has higher damage identification accuracy in the case of strong noise and small samples. The method of graph construction and pooling have certain influence on the identification results. The identification results of Path graph and Topk pooling are stable and higher than those of other combination forms.

structural health monitoring  /  damage identification  /  vibration response  /  deep learning  /  graph convolutional neural network
Xing LI, Yongpeng LUO, Xu GUO, Feiyu LIAO, Siping LU. Structural damage identification based on graph convolutional neural network under strong noise and small sample conditions[J]. Earthquake Engineering and Engineering Dynamics, 2024 , 44 (3) : 52 -60 . DOI: 10.13197/j.eeed.2024.0305
Year 2024 volume 44 Issue 3
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Article Info
doi: 10.13197/j.eeed.2024.0305
  • Receive Date:2023-01-28
  • Online Date:2026-03-30
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History
  • Received:2023-01-28
  • Revised:2023-04-24
Funding
Affiliations
    1.School of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
    2.Key Laboratory for Structural Engineering and Disaster Prevention of Fujian Province (Huaqiao University), Xiamen 361021, China
    3.Digital Fujian Intelligent Transportation Technology Internet of Things Laboratory, Fuzhou 350108, China
    4.School of Civil Engineering, Central South University, Changsha 410075, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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