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Uniaxial Static Stress Estimation for Concrete Structures Using Digital Image Correlation.

Junhwa LeeEun Jin KimSeongwoo GwonSoojin ChoSung-Han Sim
Published in: Sensors (Basel, Switzerland) (2019)
This paper proposes a static stress estimation method for concrete structures, using the stress relaxation method (SRM) in conjunction with digital image correlation (DIC). The proposed method initially requires a small hole to be drilled in the concrete surface to induce stress relaxation around the hole and, consequently, a displacement field. DIC measures this displacement field by comparing digital images taken before and after the hole-drilling. The stress level in the concrete structure is then determined by solving an optimization problem, designed to minimize the difference between the displacement fields from DIC and the one from a numerical model. Compared to the pointwise measurements by strain gauges, the full-field displacement obtained by DIC provides a larger amount of data, leading to a more accurate estimation. Our theoretical results were experimentally validated using concrete specimens, demonstrating the efficacy of the proposed method.
Keyphrases
  • deep learning
  • stress induced
  • high resolution
  • machine learning
  • mass spectrometry
  • electronic health record
  • convolutional neural network
  • big data
  • optical coherence tomography
  • ultrasound guided