Drony w inspekcji kolejowych obiektów inżynieryjnych

• Autorzy: Lesiak Piotr

Warianty tytułu

EN

Drones in the inspection of railway engineering facilities

• Języki publikacji: PL

Abstrakty

PL

W pracy pokazano walory zastosowania dronów w fotogrametrycznej inspekcji kolejowych obiektów inżynieryjnych, takich jak mosty i wiadukty. Przedstawiono konstrukcje dronów dedykowanych do takich badań. Omówiono metody mapowania takich obiektów. Wykonano pomiary wybranych parametrów geometrycznych przykładowej konstrukcji mostu kolejowego w programie komputerowym, na podstawie jego modelu fotogrametrycznego, który stwarza ramy dla poprawy zarządzania procesem inspekcji i utrzymania.

EN

The paper shows the advantages of using drones in the photogrammetric inspection of railway engineering facilities, such as bridges and viaducts. The designs of drones dedicated to such research are presented. Methods of mapping such objects are described. Measurements of selected geometric parameters of an exemplary structure of a railway bridge were measured in a computer program based on its photogrammetric model, which creates a framework for improving the management of the inspection and maintenance process.

Słowa kluczowe

PL

dron; fotogrametria; inspekcja; kolej; most; wiadukt

EN

UAV; photogrammetry; inspection; railway; bridge; viaduct

• Wydawca: Stowarzyszenie Inżynierów i Techników Komunikacji Rzeczpospolitej Polskiej
• Czasopismo: Przegląd Komunikacyjny
• Rocznik: 2021
• Tom: R. 76, nr 4
• Strony: 3--9
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Lesiak Piotr

• Wyższa Szkoła Ekonomii i Innowacji w Lublinie Wydział Transportu i Informatyki

Bibliografia

• [1] Ayelel Y.Z., Aliyaril M., Griffiths D., Droguett E. L.: Automatic Crack Segmentation for UAV-Assisted Bridge Inspection. MDPI, Energies2020,13, 6250, DOI:10.3390/en13236250.
• [2] Bojarczak P., Lesiak P.: UAVs in rail damage image diagnostics supported by deep-learning networks. Open Engineering, 2021; 11:339–348, DOI:https://doi.org/10.1515/eng-2021-0033.
• [3] Chen S., Laefer D.F., Mangina E., Zolanvari I., Byrne J.: UAV Bridge Inspection through Evaluated 3D Reconstructions. Journal of Bridge Engineering, January 2019, 2019, 24(4): 05019001, DOI: 10.1061/(ASCE)BE.1943-5592.0001343.
• [4] ChenS., Truong-Hong L., Laefer D.F., Mangina E.: Automated Bridge Deck Evaluation through UAV Derived Point Cloud. CERI-ITRN 2018, pp. 735-740.
• [5] Ciampa E., De Vito L., Pecce M.R.: Practical issues on theuse of drones for construction inspections. XXVI AIVELA National Meeting IOP Conf. Series: Journal of Physics: Conf. Series1249 (2019) 012016, DOI:10.1088/1742-6596/1249/1/012016.
• [6] Drone-based rail surveys are a ‘game changer’. CIOB, 14 August 2017, http://www.constructionmanagermagazine.com/technology/drone-based-system-game-changer-rail-surveying/, dostęp: 05.05.2021.
• [7] Garg P., Ozdagli A., Moreu F.: Railroad Bridge Inspections for Maintenance and Replacement Prioritization Using Unmanned Aerial Vehicles (UAVs) with Laser Scaning Capabilities. TRB's Rail Safety IDEA Program: Sponsoring Innovation to Improve Railroad Safety and Performance.Transportation Research Board Annual Conference, Washington D. C., January 2018.
• [8] Garg P., Roya Nasimi R., Ozdagli A., Zhang S., Mascarenas D.D.L., Taha M.R., Moreu F.: Measuring Transverse Displacements Using Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV): Development and Field Validation. MDPI, Sensors 2020, 20, 6051, DOI:10.3390/s20216051.
• [9] Gillins D.T., Parrish Ch., Gillins M.N., Simpson Ch.: Eyes in the Sky: Bridge Inspections with Unmanned Aerial Vehicles. Oregon State University, School of Civil & Construction Engineering. Report No. FHWA-OR-RD-18-11, February 2018.
• [10] Hansen H.: Drone Review: Flyability Elios, https://www.rotordronepro.com/drone-review-flyability-elios/, dostęp, 5.05.2021.
• [11] Humpe A.: Bridge Inspection with an Off-the-Shelf 3600 Camera Drone. MDPI, Drones 2020,4, 67,DOI:10.3390/drones4040067.
• [12] Id-16. Instrukcja utrzymania kolejowych obiektów inżynieryjnych na liniach kolejowych do prędkości 200/250 km/h. PKP PLK S.A, Warszawa 2014.
• [13] Ivashov S. I., Tataraidze, A. B., Razevig V.V., Smirnova E.S.: Railway Transport Infrastructure Monitoring by UAVs and Satellites. Journal of Transportation Technologies, 9, 2019, pp. 342-353.
• [14] Jongerius A.: The use of unmanned aerial vehicles to inspect bridges for Rijkswater-staat. University of Twente Faculty of Engineering Technology Civil Engineering, March 25, 2018.
• [15] Jung H.J.: Lee J.H., Kim I.H.: Challenging issues and solutions of bridge inspection technology using unmanned aerial vehicles. Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 1059802 (27 March 2018), https://doi.org/10.1117/12.2300957, dostęp: 05.05.2021.
• [16] Jung S., Choi D., Song S., Myung H.: Bridge Inspection Using Unmanned Aerial Vehicle Based on HG-SLAM: Hierarchical Graph-Based SLAM. Remote Sens. 2020, 12(18), 3022, https://doi.org/10.3390/rs12183022, dostęp 05.05.2021.
• [17] Lesiak P.: Inspekcja i utrzymanie infrastruktury kolejowej z wykorzystaniem dronów. Zeszyt 188, Tom 64, s. 41-43, DOI: 10.36137/1883P. Inspection and Maintenance of Railway Infrastructure with the Use of Unmanned Aerial Vehicles, Issue 188, Tom 64, pp. 115 - 127, 2020, DOI: 10.36137/1883E.
• [18] Lesiak P.: Drony w diagnostyce wizyjnej uszkodzeń szyn kolejowych. Przegląd Komunikacyjny, Tom LXXV, Zeszyt 9/2020, s. 15-20.
• [19] Lillian B.: UAV Carries out Extensive Inspection of Railroad Truss Bridge. Un-manned Aerial, May 18, 2017, https://unmanned-aerial.com/uav-carries-extensive-inspectionrailroad-truss-bridge, dostęp: 05.05.2021.
• [20] Moreu F., Taha M.R.: Railroad Bridge Inspections for Maintenance and Replacement Prioritization Using Unmanned Aerial Vehicles (UAVs) with Laser Scanning Capabilities. IDEA Program Final Report. Contract Number Rail Safety 32. University of New Mexico, 2016 – 2018.
• [21] Otero L.D., Gagliardo N., Dalli D., W-H Huang, Cosentino P.: Proof of Concept for Using Unmanned Aerial Vehicles for High Mast Pole and Bridge Inspections. Florida Institute of Technology, Grant No. BDV28-977-02, June 30, 2015.
• [22] PALFINGER Bridge Inspection Units, https://www.palfinger.com/en/products/bridge-inspection-units, dostęp 05.05.2021.
• [23] Pan Y., Dong Y., Wang D., Chen A., Ye Z.: Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds. Remote Sens. 2019, 11, 1204, DOI:10.3390/rs11101204.
• [24] Pix4Dmapper. The leading photogrammetry software for professional drone mapping. https://www.pix4d.com/product/pix4dmapper-photogrammetry-software, dostęp 5.05.2021.
• [25] Plotnikov M., Ni D., Price D.: The Application of Unmanned Aerial Systems In Surface Transportation - Volume II-A: Development of a Pilot Program to Integrate UAS Technology to Bridge and Rail Inspections. University of Massachusetts Amherst UMass Transportation Center, Final Report December 2019.
• [26] Rau J.Y., Hsiao K.W., Jhan J.P., Wang S.H., Fang W.C., Wang J.L.: Bridge Crack Detection Using Multi-Rotary UAV and Object-Base Image Analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-2/W6, 2017. International Conference on Unmanned Aerial Vehicles in Geomatics, 4–7 September 2017, Bonn, Germany, pp.311-318.
• [27] Seo J., Wacker J.P., Duque L.: Evaluating the Use of Drones for Timber Bridge Inspection. United States Department of Agriculture, Forest Service Forest Products Laboratory General Technical Report FPL–GTR– 258 August 2018.
• [28] Seo J., Duque L., Wacker J.: Drone-enabled bridge inspection methodology and application. Automation in Construction, Vol. 94, October 2018, pp. 112-126.
• [29] Wells J., Lovelace B.: Improving the Quality of Bridge Inspections Using Unmanned Aircraft Systems (UAS). Collins Engineers, Inc., Grant No.1027210, Minnesota Department of Transportation, July 2018.
• [30] Zollini S., Alicandro M., Dominici D., Quaresima R., Giallonardo M.: UAV Photogrammetry for Concrete Bridge Inspection Using Object-Based Image Analysis (OBIA). MDPI, Remote Sens. 2020, 12, 3180, DOI:10.3390/rs12193180.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).