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Application and Analyzation of the Vision-Based Structure Model Displacement Measuring Method in Cassette Structure Shaking Table Experiment

Auteur(s):




Médium: article de revue
Langue(s): en 
Publié dans: Advances in Civil Engineering, , v. 2020
Page(s): 1-12
DOI: 10.1155/2020/8869935
Abstrait:

In the shaking table test of large cassette structure, story drift is an essential set of experimental data. The traditional method of displacement measurement is limited to problems such as necessary full contact with the structure model for installation of sensors, large work of installation, and easily interfered by environment. The noncontact displacement measurement method, such as optical measuring technology, can solve the above problems and serve as an effective supplementary method for traditional displacement measuring in the shaking table test. This paper proposed a vison-based displacement measuring method. Predesigned artificial targets which act as sensors are installed on each floor of the cassette structure model. A high-speed industrial camera is used to acquire the series of the images of the artificial targets on the structure model during the shaking table test. A Python-OpenCV-based structural calculation program combining computer vision and machine vision is developed to extract and calculate the displacement of the artificial targets from the series of the images acquired. The proposed method is applied in a shaking table test of a reduced-scale fifteen-floor reinforced concrete cassette structure model, in which the laser displacement meter and the seismic geophone are also applied as a comparison. The experimental results acquired by the proposed method are compared with the results acquired by the laser displacement meter and the seismic geophone. The average error of the story drift obtained by the proposed vision-based measurement method is within 5% and is in good agreement with the laser displacement meter and the seismic geophone, which confirms the effectiveness of the proposed method.

Copyright: © Wang Yanhua et al. et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10526021
  • Publié(e) le:
    11.12.2020
  • Modifié(e) le:
    19.02.2021