Smart Structures and Systems

  • 제25권6호
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  • Pages.733-749
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  • 2020
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Autonomous vision-based damage chronology for spatiotemporal condition assessment of civil infrastructure using unmanned aerial vehicle

  • Mondal, Tarutal Ghosh (Lyles School of Civil Engineering, Purdue University) ;
  • Jahanshahi, Mohammad R. (Lyles School of Civil Engineering, Purdue University)
  • 투고 : 2019.10.07
  • 심사 : 2020.03.22
  • 발행 : 2020.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study presents a computer vision-based approach for representing time evolution of structural damages leveraging a database of inspection images. Spatially incoherent but temporally sorted archival images captured by robotic cameras are exploited to represent the damage evolution over a long period of time. An access to a sequence of time-stamped inspection data recording the damage growth dynamics is premised to this end. Identification of a structural defect in the most recent inspection data set triggers an exhaustive search into the images collected during the previous inspections looking for correspondences based on spatial proximity. This is followed by a view synthesis from multiple candidate images resulting in a single reconstruction for each inspection round. Cracks on concrete surface are used as a case study to demonstrate the feasibility of this approach. Once the chronology is established, the damage severity is quantified at various levels of time scale documenting its progression through time. The proposed scheme enables the prediction of damage severity at a future point in time providing a scope for preemptive measures against imminent structural failure. On the whole, it is believed that the present study will immensely benefit the structural inspectors by introducing the time dimension into the autonomous condition assessment pipeline.

키워드

과제정보

This study was supported in part by a support from Bentley Systems, Inc. The authors would like to thank Dr. Santiago Pujol, Dr. Aishwarya Puranam and Mr. Rih-Teng Wu for their help to conduct the experiment and data collection.

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