IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i23p6250-d452109.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/23/6250/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/23/6250/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. 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.
    5. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ghaneshvar Ramineni & Nafiseh Ghorbani-Renani & Kash Barker & Andrés D. González & Talayeh Razzaghi & Sridhar Radhakrishnan, 2023. "Machine learning approaches to modeling interdependent network restoration time," Environment Systems and Decisions, Springer, vol. 43(1), pages 22-35, March.
    2. Mottahedi, Adel & Sereshki, Farhang & Ataei, Mohammad & Qarahasanlou, Ali Nouri & Barabadi, Abbas, 2021. "Resilience estimation of critical infrastructure systems: Application of expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Lee, Seulbi & Choi, Minji & Lee, Hyun-Soo & Park, Moonseo, 2020. "Bayesian network-based seismic damage estimation for power and potable water supply systems," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    4. Cao, Quoc Dung & Miles, Scott B. & Choe, Youngjun, 2022. "Infrastructure recovery curve estimation using Gaussian process regression on expert elicited data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    5. Park, Jaehun & Lee, Byung Kwon, 2020. "Liner-dedicated manageability estimation for port operational reliability," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    6. Zhu, Chunli & Wu, Jianping & Liu, Mingyu & Wang, Linyang & Li, Duowei & Kouvelas, Anastasios, 2021. "Recovery preparedness of global air transport influenced by COVID-19 pandemic: Policy intervention analysis," Transport Policy, Elsevier, vol. 106(C), pages 54-63.
    7. Xu, Min & Li, Guoyuan & Chen, Anthony, 2024. "Resilience-driven post-disaster restoration of interdependent infrastructure systems under different decision-making environments," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    8. Caputo, Antonio C. & Kalemi, Bledar & Paolacci, Fabrizio & Corritore, Daniele, 2020. "Computing resilience of process plants under Na-Tech events: Methodology and application to sesmic loading scenarios," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    9. Adel Mottahedi & Farhang Sereshki & Mohammad Ataei & Ali Nouri Qarahasanlou & Abbas Barabadi, 2021. "The Resilience of Critical Infrastructure Systems: A Systematic Literature Review," Energies, MDPI, vol. 14(6), pages 1-32, March.
    10. Nozhati, Saeed & Sarkale, Yugandhar & Ellingwood, Bruce & K.P. Chong, Edwin & Mahmoud, Hussam, 2019. "Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 116-126.
    11. Albara M. Mustafa & Abbas Barabadi, 2021. "Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks," Energies, MDPI, vol. 14(15), pages 1-15, July.
    12. Farahmand, Hamed & Liu, Xueming & Dong, Shangjia & Mostafavi, Ali & Gao, Jianxi, 2022. "A Network Observability Framework for Sensor Placement in Flood Control Networks to Improve Flood Situational Awareness and Risk Management," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    13. Siqing Shan & Feng Zhao, 2023. "Social media-based urban disaster recovery and resilience analysis of the Henan deluge," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 377-405, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6250-:d:452109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.