Smart Structures and Systems

  • 제33권4호
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  • Pages.263-279
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  • 2024
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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

Review for vision-based structural damage evaluation in disasters focusing on nonlinearity

  • Sifan Wang (Department of Engineering Mechanics and Energy, University of Tsukuba) ;
  • Mayuko Nishio (Institute of Systems and Information Engineering, University of Tsukuba)
  • 투고 : 2023.10.27
  • 심사 : 2024.03.28
  • 발행 : 2024.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

With the increasing diversity of internet media, available video data have become more convenient and abundant. Related video data-based research has advanced rapidly in recent years owing to advantages such as noncontact, low-cost data acquisition, high spatial resolution, and simultaneity. Additionally, structural nonlinearity extraction has attracted increasing attention as a tool for damage evaluation. This review paper aims to summarize the research experience with the recent developments and applications of video data-based technology for structural nonlinearity extraction and damage evaluation. The most regularly used object detection images and video databases are first summarized, followed by suggestions for obtaining video data on structural nonlinear damage events. Technologies for linear and nonlinear system identification based on video data are then discussed. In addition, common nonlinear damage types in disaster events and prevalent processing algorithms are reviewed in the section on structural damage evaluation using video data uploaded on online platform. Finally, a discussion regarding some potential research directions is proposed to address the weaknesses of the current nonlinear extraction technology based on video data, such as the use of uni-dimensional time-series data as leverage to further achieve nonlinear extraction and the difficulty of real-time detection, including the fields of nonlinear extraction for spatial data, real-time detection, and visualization.

키워드

과제정보

The study described in this paper was supported by the JST SPRING Program (grant number JPMJSP2124) and the JST FOREST Program (grant number JPMJFR205T). The authors also thank to use of the video data on "Archives of E-Defense Shakingtable Experimentation Database and Information (ASEBI)", National Research Institute for Earth Science and Disaster Resilience (NIED), Japan.

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