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

Yu, Yang and Rashidi, Maria and Samali, Bijan and Mohammadi, Masoud and Nguyen, Andy ORCID: https://orcid.org/0000-0001-8739-8207 (2021) Computer vision-based classification of cracks on concrete bridges using machine learning techniques. In: 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Advanced Research and Real-World Applications (SHMII-10), 30 June - 2 July 2021, Porto, Portugal.

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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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Date Deposited: 11 Mar 2022 02:25
Last Modified: 16 Mar 2022 05:24
Uncontrolled Keywords: concrete crack; image processing; feature extraction; machine learning
Fields of Research (2020): 40 ENGINEERING > 4005 Civil engineering > 400510 Structural engineering
40 ENGINEERING > 4005 Civil engineering > 400508 Infrastructure engineering and asset management
Socio-Economic Objectives (2020): 27 TRANSPORT > 2703 Ground transport > 270308 Road infrastructure and networks
URI: http://eprints.usq.edu.au/id/eprint/47449

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