Application of Graph Convolutional Neural Networks Combined with Single-Model Decision-Making Fusion Neural Networks in Structural Damage Recognition

被引:1
|
作者
Li, Xiaofei [1 ]
Xu, Langxing [1 ]
Guo, Hainan [2 ]
Yang, Lu [1 ]
机构
[1] Dalian Maritime Univ, Coll Transportat Engn, Dalian 116026, Peoples R China
[2] Univ Technol Beijing, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China
关键词
graph convolutional neural networks; damage identification; decision-level data fusion; sensor spatial characteristics; model testing;
D O I
10.3390/s23239327
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In cases with a large number of sensors and complex spatial distribution, correctly learning the spatial characteristics of the sensors is vital for structural damage identification. Graph convolutional neural networks (GCNs), unlike other methods, have the ability to learn the spatial characteristics of the sensors, which is targeted at the above problems in structural damage identification. However, under the influence of environmental interference, sensor instability, and other factors, part of the vibration signal can easily change its fundamental characteristics, and there is a possibility of misjudging structural damage. Therefore, on the basis of building a high-performance graphical convolutional deep learning model, this paper considers the integration of data fusion technology in the model decision-making layer and proposes a single-model decision-making fusion neural network (S_DFNN) model. Through experiments involving the frame model and the self-designed cable-stayed bridge model, it is concluded that this method has a better performance of damage recognition for different structures, and the accuracy is improved based on a single model and has good damage recognition performance. The method has better damage identification performance in different structures, and the accuracy rate is improved based on the single model, which has a very good damage identification effect. It proves that the structural damage diagnosis method proposed in this paper with data fusion technology combined with deep learning has a strong generalization ability and has great potential in structural damage diagnosis.
引用
收藏
页数:20
相关论文
共 50 条
  • [21] Artificial neural networks for decision-making in urologic oncology
    Anagnostou, T
    Remzi, M
    Lykourinas, M
    Djavan, D
    EUROPEAN UROLOGY, 2003, 43 (06) : 596 - 603
  • [22] Evolving neural networks for strategic decision-making problems
    Kohl, Nate
    Miikkulainen, Risto
    NEURAL NETWORKS, 2009, 22 (03) : 326 - 337
  • [23] NEURAL NETWORKS AND MATRIX COGNITION IN MEDICAL DECISION-MAKING
    FRENSTER, JH
    CLINICAL RESEARCH, 1991, 39 (02): : A411 - A411
  • [24] Deep Learning and Neural Networks: Decision-Making Implications
    Taherdoost, Hamed
    SYMMETRY-BASEL, 2023, 15 (09):
  • [25] Reconstruction Combined Training for Convolutional Neural Networks on Character Recognition
    Chen, Li
    Wang, Song
    Fan, Wei
    Sun, Jun
    Satoshi, Naoi
    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR), 2015, : 431 - 435
  • [26] Application of Sequential Neural Networks to Predict the Approximation Degree of Decision-making Systems
    Szkola, Jaroslaw
    Artiemjew, Piotr
    ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 3, 2022, : 984 - 989
  • [27] Research on the application of neural networks in macroeconomic forecasting and strategic management decision-making
    Xue, Qianqian
    Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
  • [28] Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks
    Scholz, Veronika
    Winkler, Peter
    Hornig, Andreas
    Gude, Maik
    Filippatos, Angelos
    SENSORS, 2021, 21 (06) : 1 - 15
  • [29] Convolutional Neural Networks for Structural Damage Identification in Assembled Buildings
    You, Chunhua
    Liu, Wenxiang
    Hou, Lei
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [30] Convolutional Neural Networks for Structural Damage Identification in Assembled Buildings
    You, Chunhua
    Liu, Wenxiang
    Hou, Lei
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022