Abstract During the long-term operation of hydro-junction infrastructure, water flow erosion causes concrete surfaces to crack, resulting in seepage, spalling, and rebar exposure. To ensure infrastructure safety, detecting such damage is critical. We propose a highly accurate damage detection method using a deep convolutional neural network with transfer learning. First, we collected images from hydro-junction infrastructure using a high-definition camera. Second, we preprocessed the images using an image expansion method. Finally, we modified the structure of Inception-v3 and trained the network using transfer learning to detect damage. The experiments show that the accuracy of the proposed damage detection method is 96.8%, considerably higher than the accuracy of a support vector machine. The results demonstrate that our damage detection method achieves better damage detection performance.


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    Titel :

    Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning


    Beteiligte:
    Feng, Chuncheng (Autor:in) / Zhang, Hua (Autor:in) / Wang, Shuang (Autor:in) / Li, Yonglong (Autor:in) / Wang, Haoran (Autor:in) / Yan, Fei (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    03.09.2019


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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