Artificial intelligence-empowered pipeline for image-based inspection of concrete structures

Inspection of civil infrastructure is a major challenge to engineers due to the limitations in existing practice, which are as laborious, time-consuming and prone to error. To address these issues, the authors have applied deep learning for image-based inspection of concrete defects of civil infrastructure, and have established an artificial intelligence-empowered inspection pipeline methodology. This innovative approach comprises anomaly detection, anomaly extraction and defect classification. The anomaly detection and extraction are used to identify defect regions from the enormous volume of image datasets, which used to be the common challenges encountered in automated visual inspections. The search space of defects is substantially reduced, i.e., at least 60% of the original volume of image datasets, with an average hit rate of ~88.7% and an average false alarm rate of ~14.2%. Following that, deep learning-based classifiers are used to categorize defects into appropriate classes. The assessment results show that the proposed inspection pipeline exhibits great capability in detecting, extracting and classifying defects subjected to various environmental and operational conditions, including lighting condition, camera distance and capturing angle, with an average testing accuracy of 95.6%.

• Record URL:
  https://doi.org/10.1016/j.autcon.2020.103372
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0926580520309523
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09265805
• Supplemental Notes:
  • © 2020 Elsevier B.V. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Chow, Jun Kang
  • Su, Zhaoyu
  • Wu, Jimmy
  • Li, Zhaofeng
  • Tan, Pin Siang
  • Liu, Kuan-fu
  • Mao, Xin
  • Wang, Yu-Hsing
• Publication Date: 2020-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Automation in Construction
  • Volume: 120
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0926-5805
  • Serial URL: http://www.sciencedirect.com/science/journal/09265805

Subject/Index Terms

• TRT Terms: Artificial intelligence; Classification; Concrete structures; Cracking; Defects; Inspection; Machine learning; Spalling
• Subject Areas: Materials; Pipelines;

Filing Info

• Accession Number: 01755040
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 21 2020 9:52AM