Damage identification for frame structures using vision-based measurement

Jia Guo, Jian Jiao, Kohei Fujita, Izuru Takewaki

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Extracting physical parameters for damage identification problems from full-field measurements is a promising research because of the recent spread of vision-based measurement techniques in the experimental mechanics. This paper presents a vision-based measurement framework using the camera system for damage identification. The framework is composed of four procedures: camera calibration, image processing, system identification and sensitivity analysis. In contrast to traditional finite-point measurements, the camera system allows considerably greater non-contact measurement flexibility. Such flexibility has two important benefits: first, less number of modes is required for modal-based damage identification problems; and second, more physical parameters could be extracted, taking advantage of the plentiful experimental data. A laboratory test comparing the camera system to traditional accelerometer measurement is conducted to confirm the above advantages. Further statistic analysis shows that the major drawback of this technique is that the camera system presents high levels of noise in small vibration responses at higher frequencies. Suitable strategies to circumvent this disadvantage are developed. Moreover, a technique for practical camera calibration without the requirement that the objective plane should be strictly perpendicular to the camera axis is also demonstrated and verified by the proposed laboratory test.

Original languageEnglish
Article number109634
JournalEngineering Structures
Publication statusPublished - 2019 Nov 15
Externally publishedYes


  • Damage identification
  • Homography estimation
  • Sensitivity analysis
  • Vision-based measurement

ASJC Scopus subject areas

  • Civil and Structural Engineering


Dive into the research topics of 'Damage identification for frame structures using vision-based measurement'. Together they form a unique fingerprint.

Cite this