Enhancing visual-based bridge condition assessment for concrete crack evaluation using image processing techniques

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Author(s)
Wi, H
Nguyen, V
Lee, J
Guan, H
Loo, YC
Blumenstein, M
Year published
2013
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Show full item recordAbstract
Condition assessment is one of the most essential practices in bridge asset management to maintain the safety and durability of structures. Routine bridge inspection, a visual-based method, is regularly performed by qualified inspectors to determine the condition of individual bridge elements manually using bridge inspection standards. However, the quality of a visual-based condition assessment relies heavily on the inspector's knowledge and experience. The research presented here focuses on the development of an enhanced method to minimise the shortcomings of visual-based inspection. In this paper, we investigate the ...
View more >Condition assessment is one of the most essential practices in bridge asset management to maintain the safety and durability of structures. Routine bridge inspection, a visual-based method, is regularly performed by qualified inspectors to determine the condition of individual bridge elements manually using bridge inspection standards. However, the quality of a visual-based condition assessment relies heavily on the inspector's knowledge and experience. The research presented here focuses on the development of an enhanced method to minimise the shortcomings of visual-based inspection. In this paper, we investigate the performance of RBF-kernel support vector machines (SVMs), a supervised machine learning technique, to increase the reliability of visual-based bridge inspection. The results of this study can contribute to minimising the shortcomings of current visual-based bridge inspection practices.
View less >
View more >Condition assessment is one of the most essential practices in bridge asset management to maintain the safety and durability of structures. Routine bridge inspection, a visual-based method, is regularly performed by qualified inspectors to determine the condition of individual bridge elements manually using bridge inspection standards. However, the quality of a visual-based condition assessment relies heavily on the inspector's knowledge and experience. The research presented here focuses on the development of an enhanced method to minimise the shortcomings of visual-based inspection. In this paper, we investigate the performance of RBF-kernel support vector machines (SVMs), a supervised machine learning technique, to increase the reliability of visual-based bridge inspection. The results of this study can contribute to minimising the shortcomings of current visual-based bridge inspection practices.
View less >
Journal Title
IABSE Symposium Report
Volume
101
Issue
1
Publisher URI
Copyright Statement
© 2013 IABSE. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Subject
Civil engineering not elsewhere classified