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Deep learning based automated segmentation of air-void system in hardened concrete surface using three dimensional reconstructed images

Construction and Building Materials, ISSN: 0950-0618, Vol: 324, Page: 126717
2022
  • 6
    Citations
  • 0
    Usage
  • 12
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    6
    • Citation Indexes
      6
  • Captures
    12

Article Description

The automated air-void detection methods specified in the ASTM C457 require the aid of contrast enhancement which is time consuming and labor intensive. This study investigated the utilization of three-dimensional (3D) reconstruction and Deep Convolution Neural Network (DCNN) methods to detect the air voids in hardened concrete surfaces without the use of contrast enhancement. The experimental results showed that the DCNN could accurately distinguish air voids from hardened concrete images with the detection accuracy of over 0.9 in only less than a minute. The accuracy rates for air content, specific surface, and spacing factor were 0.92, 0.91, and 0.89, respectively.

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