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Automatic Crack Segmentation for UAV-Assisted Bridge Inspection

Author

Listed:
  • Yonas Zewdu Ayele

    (Faculty of Engineering, Østfold University College, 1671 Fredrikstad, Norway)

  • Mostafa Aliyari

    (Faculty of Engineering, Østfold University College, 1671 Fredrikstad, Norway)

  • David Griffiths

    (Department of Civil, Environmental and Geomatic Engineering, Faculty of Engineering, University College London, London WC1E 6BT, UK)

  • Enrique Lopez Droguett

    (Department of Mechanical Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago 850, Chile)

Abstract

Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.

Suggested Citation

  • Yonas Zewdu Ayele & Mostafa Aliyari & David Griffiths & Enrique Lopez Droguett, 2020. "Automatic Crack Segmentation for UAV-Assisted Bridge Inspection," Energies, MDPI, vol. 13(23), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6250-:d:452109
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    References listed on IDEAS

    as
    1. Barabadi, A. & Ayele, Y.Z., 2018. "Post-disaster infrastructure recovery: Prediction of recovery rate using historical data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 209-223.
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    Cited by:

    1. Heather Holden & Maha Hussein Abdallah & Dane Rowlands, 2023. "A study to assess the applicability of using remote sensing to minimize service interruption of Canadian port physical infrastructure," Journal of Transportation Security, Springer, vol. 16(1), pages 1-18, December.
    2. Kamal Achuthan & Nick Hay & Mostafa Aliyari & Yonas Zewdu Ayele, 2021. "A Digital Information Model Framework for UAS-Enabled Bridge Inspection," Energies, MDPI, vol. 14(19), pages 1-17, September.
    3. Mostafa Aliyari & Enrique Lopez Droguett & Yonas Zewdu Ayele, 2021. "UAV-Based Bridge Inspection via Transfer Learning," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    4. Jonghyeon Yoon & Hyunkyu Shin & Mihwa Song & Heungbae Gil & Sanghyo Lee, 2022. "A Crack Width Measurement Method of UAV Images Using High-Resolution Algorithms," Sustainability, MDPI, vol. 15(1), pages 1-11, December.
    5. Faten Aljalaud & Heba Kurdi & Kamal Youcef-Toumi, 2023. "Autonomous Multi-UAV Path Planning in Pipe Inspection Missions Based on Booby Behavior," Mathematics, MDPI, vol. 11(9), pages 1-23, April.
    6. Matija Krznar & Danijel Pavković & Mihael Cipek & Juraj Benić, 2021. "Modeling, Controller Design and Simulation Groundwork on Multirotor Unmanned Aerial Vehicle Hybrid Power Unit," Energies, MDPI, vol. 14(21), pages 1-26, November.

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