Computer vision-based classification of cracks on concrete bridges using machine learning techniques

Paper

Yu, Yang, Rashidi, Maria, Samali, Bijan, Mohammadi, Masoud and Nguyen, Andy. 2021. "Computer vision-based classification of cracks on concrete bridges using machine learning techniques." Cunha, A. and Caetano, E. (ed.) 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Advanced Research and Real-World Applications (SHMII-10). Porto, Portugal 30 Jun - 02 Jul 2021 Winnipeg, Canada.
Paper/Presentation Title

Computer vision-based classification of cracks on concrete bridges using machine learning techniques

Presentation TypePaper
AuthorsYu, Yang (Author), Rashidi, Maria (Author), Samali, Bijan (Author), Mohammadi, Masoud (Author) and Nguyen, Andy (Author)
EditorsCunha, A. and Caetano, E.
Journal or Proceedings TitleProceedings of the 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10)
ERA Conference ID43541
Number of Pages6
Year2021
Place of PublicationWinnipeg, Canada
Web Address (URL) of Paperhttps://web.fe.up.pt/~shmii10//ficheiros/eBook_SHMII_2021.pdf
Conference/Event10th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Advanced Research and Real-World Applications (SHMII-10)
International Conference on Structural Health Monitoring of Intelligent Infrastructure
Event Details
International Conference on Structural Health Monitoring of Intelligent Infrastructure
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Event Details
10th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Advanced Research and Real-World Applications (SHMII-10)
Event Date
30 Jun 2021 to end of 02 Jul 2021
Event Location
Porto, Portugal
Abstract

Concrete crack is a significant indicator related to the durability and serviceability of concrete civil infrastructure such as dams, bridges and tunnels. Current inspection of concrete structures is based on manual visual operation, which is not effective in safety, cost and reliability. This research aims to address the problems in traditional inspection of concrete structures by proposing a novel automatic crack identification approach, which intelligently integrates both image processing and machine learning techniques. Through the crack-sensitive feature extraction and model self-learning, the proposed method has higher identification accuracy than conventional inspection method, which has been proved by the experimental verification.

Keywordsconcrete crack; image processing; feature extraction; machine learning
ANZSRC Field of Research 2020400510. Structural engineering
400508. Infrastructure engineering and asset management
Byline AffiliationsWestern Sydney University
School of Civil Engineering and Surveying
Institution of OriginUniversity of Southern Queensland
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