Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Structural damage identification based on autoencoder neural networks and deep learning

    Access Status
    Fulltext not available
    Authors
    Pathirage, C.
    Li, Jun
    Li, L.
    Hao, Hong
    Liu, Wan-Quan
    Ni, P.
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Pathirage, C. and Li, J. and Li, L. and Hao, H. and Liu, W. and Ni, P. 2018. Structural damage identification based on autoencoder neural networks and deep learning. Engineering Structures. 172: pp. 13-28.
    Source Title
    Engineering Structures
    DOI
    10.1016/j.engstruct.2018.05.109
    ISSN
    0141-0296
    School
    School of Civil and Mechanical Engineering (CME)
    URI
    http://hdl.handle.net/20.500.11937/68711
    Collection
    • Curtin Research Publications
    Abstract

    © 2018 Elsevier Ltd Artificial neural networks are computational approaches based on machine learning to learn and make predictions based on data, and have been applied successfully in diverse applications including structural health monitoring in civil engineering. It is difficult to optimize the weights in the neural networks that have multiple hidden layers due to the vanishing gradient issue. This paper proposes an autoencoder based framework for structural damage identification, which can support deep neural networks and be utilized to obtain optimal solutions for pattern recognition problems of highly non-linear nature, such as learning a mapping between the vibration characteristics and structural damage. Two main components are defined in the proposed framework, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the original input vector while preserving the required necessary information, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. Vibration characteristics, such as natural frequencies and mode shapes, are used as the input and the structural damage are considered as the output vector. A pre-training scheme is performed to train the hidden layers in the autoencoders layer by layer, and fine tuning is conducted to optimize the whole network. Numerical and experimental investigations on steel frame structures are conducted to demonstrate the accuracy and efficiency of the proposed framework, comparing with the traditional ANN methods.

    Related items

    Showing items related by title, author, creator and subject.

    • Deep residual network framework for structural health monitoring
      Wang, Ruhua; Chencho,; An, Senjian ; Li, Jun ; Li, Ling ; Hao, Hong ; Liu, Wan-Quan (2021)
      Convolutional neural networks have been widely employed for structural health monitoring and damage identification. The convolutional neural network is currently considered as the state-of-the-art method for structural ...
    • Application of deep autoencoder model for structural condition monitoring
      Pathirage, C.; Li, Jun; Li, L.; Hao, Hong; Liu, Wan-Quan (2018)
      Damage detection in structures is performed via vibration based structural identification. Modal information, such as frequencies and mode shapes, are widely used for structural damage detection to indicate the health ...
    • Development and application of a deep learning–based sparse autoencoder framework for structural damage identification
      Pathirage, C.; Li, Jun; Li, L.; Hao, Hong; Liu, Wan-Quan; Wang, R. (2019)
      © The Author(s) 2018. This article proposes a deep sparse autoencoder framework for structural damage identification. This framework can be employed to obtain the optimal solutions for some pattern recognition problems ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.