한국BIM학회 논문집 (Journal of KIBIM)

  • 제13권4호
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  • Pages.64-73
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  • 2023
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  • 2288-1697(pISSN)
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  • 2288-1697(eISSN)
한국BIM학회 (Korean Institute of Building Information Modeling)
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UAV와 BIM 정보를 활용한 시설물 외관 손상의 위치 측정 방법

Structural Damage Localization for Visual Inspection Using Unmanned Aerial Vehicle with Building Information Modeling Information

  • 이용주 (명지대학교 토목환경공학과) ;
  • 박만우 (명지대학교 토목환경공학과)
  • 투고 : 2023.11.14
  • 심사 : 2023.11.22
  • 발행 : 2023.12.31
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초록

This study introduces a method of estimating the 3D coordinates of structural damage from the detection results of visual inspection provided in 2D image coordinates using sensing data of UAV and 3D shape information of BIM. This estimation process takes place in a virtual space and utilizes the BIM model, so it is possible to immediately identify which member of the structure the estimated location corresponds to. Difference from conventional structural damage localization methods that require 3D scanning or additional sensor attachment, it is a method that can be applied locally and rapidly. Measurement accuracy was calculated through the distance difference between the measured position measured by TLS (Terrestrial Laser Scanner) and the estimated position calculated by the method proposed in this study, which can determine the applicability of this study and the direction of future research.

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과제정보

이 연구는 국토교통부/국토교통과학기술진흥원이 시행하고 한국도로공사가 총괄하는 "스마트건설기술개발 국가R&D사업(과제번호 RS-2020-KA158708)"의 지원으로 수행하였습니다.

참고문헌

  1. Agdas, D., Rice, J. A., Martinez, J. R., Lasa, I. R. (2016). Comparison of Visual Inspection and Structural-Health Monitoring as Bridge Condition Assessment Methods, Journal of Performance of Constructed Facilities, 30(3), Article 04015049.
  2. Dorafshan, S., Maguire, M. (2018). Bridge Inspection: Human Performance, Unmanned Aerial Systems and Automation, Journal of Civil Structural Health Monitoring, 8, pp. 443-476. https://doi.org/10.1007/s13349-018-0285-4
  3. Kim, I. H., Jeon, H., Baek, S. C., Hong, W. H., Jung, H. J. (2018). Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle, Sensors, 18, Article 1881.
  4. Lim, R. S., La, H. M., Shan, Z., Sheng, W. (2011). Developing a Crack Inspection Robot for Bridge Maintenance, 2011 IEEE International Conference on Robotics and Automation, pp. 6288-6293.
  5. Lim, R. S., La, H. M., Sheng, W. (2014). A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance, IEEE Transactions on Automation Science and Engineering, 11(2), pp. 367-378. https://doi.org/10.1109/TASE.2013.2294687
  6. Liu, Y. F., Nie, X., Fan, J. S., Liu, X. G. (2020). Image-based Crack Assessment of Bridge Piers Using Unmanned Aerial Vehicles and Three-dimensional Scene Reconstruction, Computer-Aided Civil and Infrastructure Engineering, 35, pp. 511-529. https://doi.org/10.1111/mice.12501
  7. Maria, A., Tommaselli, G., Luis, M., Reiss, L. (2005). A Photogrammetric Method for Single Image Orientation and Measurement, Photogrammetric Engineering & Remote Sensing, 71, pp. 727-732. https://doi.org/10.14358/PERS.71.6.727
  8. Martinez, J., Ordonez, C., Arias, P., Armesto, J. (2011). Non-contact 3D Measurement of Buildings Through Close Range Photogrammetry and a Laser Distance Meter, Photogrammetric Engineering & Remote Sensing, 77, pp. 805-811. https://doi.org/10.14358/PERS.77.8.805
  9. Saleem, M. R., Park, J. W., Lee, J. H., Jung, H. J., Sarwar, M. Z. (2021). Instant Bridge Visual Inspection using an Unmanned Aerial Vehicle by Image Capturing and Geo-tagging System and Deep Convolutional Neural Network, Structural Health Monitoring, 20, pp. 1760-1777. https://doi.org/10.1177/1475921720932384
  10. Tian, Y., Chen, C., Sagoe-Crentsil, K., Zhang, J., Duan, W., (2022). Intelligent Robotic Systems for Structural Health Monitoring: Applications and Future Trends, Automation in Construction, 139, 104273.
  11. Yoon, S., Gwon, G. H., Lee, J. H., Jung, H. J. (2021). Three-dimensional Image Coordinate-based Missing Region of Interest Area Detection and Damage Localization for Bridge Visual Inspection Using Unmanned Aerial Vehicles, Structural Health Monitoring, 20, pp. 1462-1475. https://doi.org/10.1177/1475921720918675
  12. Zhao, S., Kang, F., Li, J. (2022). Concrete Dam Damage Detection and Localisation Based on YOLOv5s-HSC and Photogrammetric 3D Reconstruction, Automation in Construction, 143, Article 104555.