IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i9p1599-d226349.html
   My bibliography  Save this article

Automatic and Visual Processing Method of Non-Contact Monitoring for Circular Stormwater Sewage Tunnels Based on LiDAR Data

Author

Listed:
  • Xiongyao Xie

    (Department of Geotechnical Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China)

  • Mingrui Zhao

    (Department of Geotechnical Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China)

  • Jiamin He

    (Department of Geotechnical Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China)

  • Biao Zhou

    (Department of Geotechnical Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China)

Abstract

The application of Light Detection And Ranging (LiDAR) technology has become increasingly extensive in tunnel structure monitoring. The proposed processing method aims to carry out non-contact monitoring for circular stormwater sewage tunnels and provides an efficient workflow. This allows the automatic processing of raw point data and the acquisition of visualization results to analyze the health state of a tunnel within a short period of time. The proposed processing method employs a series of algorithms to extract the point cloud of a single tunnel segment without obvious noise by main three steps: axis acquisition, segment extraction, and denoising. The tunnel axis is extracted by fitting boundaries of the tunnel point cloud projection in the plane. With the guidance of the axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of the tunnel point cloud which corresponds to a single tunnel segment. Then, the noise in every single point cloud segment is removed by clustering the algorithm twice, based on the distance and intensity. Finally, clean point clouds of tunnel segments are processed by an effective deformation extraction processor to determine the ovality and to get a three-dimensional visual deformation nephogram. The proposed method can significantly improve the efficiency of LiDAR data processing and extend the application of LiDAR technology in circular stormwater sewage tunnel monitoring.

Suggested Citation

  • Xiongyao Xie & Mingrui Zhao & Jiamin He & Biao Zhou, 2019. "Automatic and Visual Processing Method of Non-Contact Monitoring for Circular Stormwater Sewage Tunnels Based on LiDAR Data," Energies, MDPI, vol. 12(9), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1599-:d:226349
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/9/1599/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/9/1599/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diane Palmer & Elena Koumpli & Ian Cole & Ralph Gottschalg & Thomas Betts, 2018. "A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry," Energies, MDPI, vol. 11(12), pages 1-22, December.
    2. Soledad Le Clainche & Luis S. Lorente & José M. Vega, 2018. "Wind Predictions Upstream Wind Turbines from a LiDAR Database," Energies, MDPI, vol. 11(3), pages 1-15, March.
    Full references (including those not matched with items on IDEAS)

    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. Dongheon Shin & Kyungnam Ko, 2019. "Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement," Energies, MDPI, vol. 12(6), pages 1-15, March.
    2. Yingli Wu & Xin Li & Qingquan Liu & Guangji Tong, 2022. "The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1269-1292, December.
    3. Hannes Koch & Stefan Lechner & Sebastian Erdmann & Martin Hofmann, 2022. "Assessing the Potential of Rooftop Photovoltaics by Processing High-Resolution Irradiation Data, as Applied to Giessen, Germany," Energies, MDPI, vol. 15(19), pages 1-17, September.
    4. Sredenšek, Klemen & Štumberger, Bojan & Hadžiselimović, Miralem & Mavsar, Primož & Seme, Sebastijan, 2022. "Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method," Energy, Elsevier, vol. 242(C).
    5. Liu, Zhengguang & Guo, Zhiling & Song, Chenchen & Du, Ying & Chen, Qi & Chen, Yuntian & Zhang, Haoran, 2023. "Business model comparison of slum-based PV to realize low-cost and flexible power generation in city-level," Applied Energy, Elsevier, vol. 344(C).
    6. Marcela Bindzarova Gergelova & Slavomir Labant & Stefan Kuzevic & Zofia Kuzevicova & Henrieta Pavolova, 2020. "Identification of Roof Surfaces from LiDAR Cloud Points by GIS Tools: A Case Study of Lučenec, Slovakia," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    7. Vytautas Bocullo & Linas Martišauskas & Darius Pupeikis & Ramūnas Gatautis & Rytis Venčaitis & Rimantas Bakas, 2023. "UAV Photogrammetry Application for Determining the Influence of Shading on Solar Photovoltaic Array Energy Efficiency," Energies, MDPI, vol. 16(3), pages 1-19, January.
    8. Primož Mavsar & Klemen Sredenšek & Bojan Štumberger & Miralem Hadžiselimović & Sebastijan Seme, 2019. "Simplified Method for Analyzing the Availability of Rooftop Photovoltaic Potential," Energies, MDPI, vol. 12(22), pages 1-17, November.
    9. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
    10. De Cillis, Giovanni & Cherubini, Stefania & Semeraro, Onofrio & Leonardi, Stefano & De Palma, Pietro, 2022. "The influence of incoming turbulence on the dynamic modes of an NREL-5MW wind turbine wake," Renewable Energy, Elsevier, vol. 183(C), pages 601-616.
    11. Myeongchan Oh & Hyeong-Dong Park, 2019. "Optimization of Solar Panel Orientation Considering Temporal Volatility and Scenario-Based Photovoltaic Potential: A Case Study in Seoul National University," Energies, MDPI, vol. 12(17), pages 1-17, August.
    12. Zhong, Qing & Nelson, Jake R. & Tong, Daoqin & Grubesic, Tony H., 2022. "A spatial optimization approach to increase the accuracy of rooftop solar energy assessments," Applied Energy, Elsevier, vol. 316(C).
    13. Soledad Le Clainche & José M. Vega, 2018. "Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods," Complexity, Hindawi, vol. 2018, pages 1-21, December.
    14. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2018. "Data Science and Big Data in Energy Forecasting," Energies, MDPI, vol. 11(11), pages 1-2, November.
    15. Soledad Le Clainche, 2019. "Prediction of the Optimal Vortex in Synthetic Jets," Energies, MDPI, vol. 12(9), pages 1-26, April.
    16. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
    17. De Cillis, Giovanni & Semeraro, Onofrio & Leonardi, Stefano & De Palma, Pietro & Cherubini, Stefania, 2022. "Dynamic-mode-decomposition of the wake of the NREL-5MW wind turbine impinged by a laminar inflow," Renewable Energy, Elsevier, vol. 199(C), pages 1-10.
    18. Walch, Alina & Castello, Roberto & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2020. "Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty," Applied Energy, Elsevier, vol. 262(C).

    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:12:y:2019:i:9:p:1599-:d:226349. 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.