[1] Tu Yongming, Lu Senlu, Wang Chao,. Damage identification of steel truss bridgesbased on deep belief network [J]. Journal of Southeast University (English Edition), 2022, 38 (4): 392-400. [doi:10.3969/j.issn.1003-7985.2022.04.008]

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Damage identification of steel truss bridgesbased on deep belief network()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

38

Issue:

2022 4

Page:

392-400

Research Field:

Traffic and Transportation Engineering

Publishing date:

2022-12-20

Info

Title:

Damage identification of steel truss bridgesbased on deep belief network

Author(s):

Tu Yongming;  Lu Senlu;  Wang Chao

Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 211189, China
National Prestressing Engineering Research Center, Southeast University, Nanjing 211189, China
School of Civil Engineering, Southeast University, Nanjing 211189, China

Keywords:

deep learning;  restricted Boltzmann machine;  deep belief network;  structural damage identification

PACS:

U446.3

DOI:

10.3969/j.issn.1003-7985.2022.04.008

Abstract:

To improve the accuracy and anti-noise ability of the structural damage identification method, a bridge damage identification method is proposed based on a deep belief network(DBN). The output vector is used to establish the nonlinear mapping relationship between the mode shape and structural damage. The hidden layer of the DBN is trained through a layer-by-layer pre-training. Finally, the backpropagation algorithm is used to fine-tune the entire network. The method is validated using a numerical model of a steel truss bridge. The results show that under the influence of noise and modeling uncertainty, the damage identification method based on the DBN can identify the accurate damage location and degree identification compared with the traditional damage identification method based on an artificial neural network.

References:

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Memo

Memo:

Biography: Tu Yongming(1978—), male, doctor, professor, tuyongming@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No. 51378104).
Citation: Tu Yongming, Lu Senlu, Wang Chao. Damage identification of steel truss bridges based on deep belief network[J].Journal of Southeast University(English Edition), 2022, 38(4):392-400.DOI:10.3969/j.issn.1003-7985.2022.04.008.

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