Enhancing Visual-based Bridge Condition Assessment for Concrete Crack Evaluation Using Image Processing Techniques
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Détails bibliographiques
Auteur(s): |
Huiju Wi
Vu Nguyen Jaeho Lee Hong Guan Yew-Chaye Loo Michael Blumenstein |
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Médium: | papier de conférence | ||||
Langue(s): | anglais | ||||
Conférence: | IABSE Symposium: Long Span Bridges and Roofs - Development, Design and Implementation, Kolkata, India, 24-27 September 2013 | ||||
Publié dans: | IABSE Symposium Kolkata 2013 | ||||
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Page(s): | 1-7 | ||||
Nombre total de pages (du PDF): | 7 | ||||
Année: | 2013 | ||||
DOI: | 10.2749/222137813815776287 | ||||
Abstrait: |
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. |