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Research on hierarchical identification of structural damage based on dynamic characteristics by deep belief networks
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Liangliang CHANG1, Wenkai JIANG2, Hanqing YANG3, Xing SUN4, Wei HE3
Earthquake Engineering and Engineering Dynamics | 2024, 44(2) : 61 - 71
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Earthquake Engineering and Engineering Dynamics | 2024, 44(2): 61-71
Research on hierarchical identification of structural damage based on dynamic characteristics by deep belief networks
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Liangliang CHANG1, Wenkai JIANG2, Hanqing YANG3, Xing SUN4, Wei HE3
Affiliations
  • 1.Xuchang Construction Investment Co., Ltd., Xuchang 461000, China
  • 2.China Railway Fourth Survey and Design Institute Group Co., Ltd., Wuhan 430063, China
  • 3.School of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
  • 4.China Railway 16th Bureau Group Co., Ltd., Beijing 100018, China
doi: 10.13197/j.eeed.2024.0207
Outline
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To identify structural damage efficiently and accurately, a hierarchical identification method of structural damage based on structural dynamic characteristics and deep belief network is proposed by combining machine learning with intelligent algorithm, and the damage position and degree are identified in turn. In order to identify the damage location, a 6-element vector is established by using the first three vertical vibration frequencies of the structure and the third modal displacement of a single node, and the damage location is identified by using the 6-element vector as input parameters. To identify the damage degree, the first three natural frequencies and modal displacements of vertical vibration or the 6 nodes modal curvature differences are used as parameters to input the depth confidence network to identify the damage degree. A simple-supported beam is taken as a model to verify it. It is shown that the damage position recognition accuracy can reach 100% even if the noise level reaches 10%. When identifying the damage degree, the deep belief network based on six-node modal curvature difference has strong noise resistance. The maximum relative error of damage degree prediction is less than 5.08% and the mean square error is 0.4878 under the noise of 15%. Compared with BP neural network, the prediction ability of BP neural network is better than that of deep belief network when there is no noise. Under the same noise level, the prediction ability of depth belief network is obviously better than that of BP neural network, which shows that the hierarchical identification method of structural damage based on structural dynamic characteristics and deep belief networks has strong robustness and high accuracy of identification results.

deep belief networks(DBN)  /  damage identification  /  noise immunity  /  BP neural network
Liangliang CHANG, Wenkai JIANG, Hanqing YANG, Xing SUN, Wei HE. Research on hierarchical identification of structural damage based on dynamic characteristics by deep belief networks[J]. Earthquake Engineering and Engineering Dynamics, 2024 , 44 (2) : 61 -71 . DOI: 10.13197/j.eeed.2024.0207
Year 2024 volume 44 Issue 2
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Article Info
doi: 10.13197/j.eeed.2024.0207
  • Receive Date:2022-10-11
  • Online Date:2026-03-30
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History
  • Received:2022-10-11
  • Revised:2023-05-17
Funding
Affiliations
    1.Xuchang Construction Investment Co., Ltd., Xuchang 461000, China
    2.China Railway Fourth Survey and Design Institute Group Co., Ltd., Wuhan 430063, China
    3.School of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
    4.China Railway 16th Bureau Group Co., Ltd., Beijing 100018, China
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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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