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Vision-Based Damage Detection Using Inclination Angles and Curvature

EasyChair Preprint no. 5847

14 pagesDate: June 18, 2021

Abstract

This paper presents damage detection techniques for structural health monitoring of horizontal structures using computer vision. A technique based on the derivation of curvature from the second order polynomial equations of the deflection curve is introduced. The technique, as well as inclination angles, and the primary deflection data are applied for damage detection on a simply supported laboratory beam subjected to a point load at its midspan. The beam is loaded and unloaded at intact and damaged states. Measurements are obtained with a smartphone. The measurement resolution is 1mm/px – a relatively low value. Measurements are pre-processed for measurement noise. Results show that damage can be detected using all three responses analysis techniques. The curvature and inclination angle techniques outperform the deflection technique, especially for damage identification.

Keyphrases: civil structural health monitoring, computer vision, condition assessment, curvature profile, damage detection, damage detection technique, Deflection Curve, displacement measurement, Image Acquisition, inclination angle, low cost, measurement accuracy, measurement noise, measurement resolution, Structural Health Monitoring, structural response, target displacement, target location, target set, Vision based system, vision-based deformation monitoring

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:5847,
  author = {Chidiebere Brendan Obiechefu and Rolands Kromanis and Fouad Mohammad and Zakwan Arab},
  title = {Vision-Based Damage Detection Using Inclination Angles and Curvature},
  howpublished = {EasyChair Preprint no. 5847},

  year = {EasyChair, 2021}}
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