Journal
KSCE JOURNAL OF CIVIL ENGINEERING
Volume 23, Issue 10, Pages 4493-4502Publisher
KOREAN SOCIETY OF CIVIL ENGINEERS-KSCE
DOI: 10.1007/s12205-019-0437-z
Keywords
hydro-junction infrastructure; damage detection; deep convolutional neural network; transfer learning; structural health monitoring; concrete surface defect
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Funding
- Sichuan Energy Internet Research Center of Tsinghua University
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During the long-term operation of hydro-junction infrastructure, water flow erosion causes concrete surfaces to crack, resulting in seepage, spalling, and rebar exposure. To ensure infrastructure safety, detecting such damage is critical. We propose a highly accurate damage detection method using a deep convolutional neural network with transfer learning. First, we collected images from hydro-junction infrastructure using a high-definition camera. Second, we preprocessed the images using an image expansion method. Finally, we modified the structure of Inception-v3 and trained the network using transfer learning to detect damage. The experiments show that the accuracy of the proposed damage detection method is 96.8%, considerably higher than the accuracy of a support vector machine. The results demonstrate that our damage detection method achieves better damage detection performance.
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