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PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Edited by: Y. Tsompanakis, J. Kruis and B.H.V. Topping
Automated Recognition of Surface Defects in Subway Systems
T. Dawood, Z. Zhu and T. Zayed
Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada
T. Dawood, Z. Zhu, T. Zayed, "Automated Recognition of Surface Defects in Subway Systems", in Y. Tsompanakis, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 13, 2015. doi:10.4203/ccp.109.13
Keywords: image processing, concrete infrastructure, subway networks, water leakage, condition assessment, visual inspection.
Subway networks monitoring and maintenance present substantial challenges for managers and engineers. Major subway authorities in the world have several tunnels running under rivers. Water leakage through soil has been considered as the main cause of concrete degradation in subway facilities. Other structural defects are derived from water intrusion, such as rebar corrosion, spalling, and delamination. Therefore, condition assessment of subway networks represents a challenging task in the sustainability of a sound concrete infrastructure. Visual inspection techniques are considered the principal methods used in the condition evaluation of civil infrastructure. These methods are time-consuming, expensive, and dependent inherently on subjective criteria. Automating the current practice is expected to provide more objective, accurate, and quantitative results. This paper presents a defect-based condition assessment model for subway networks based on image processing methods. The model performs damage identification and quantification of moisture marks on the external surface of concrete through different image enhancement algorithms such as histogram equalization, edge detection and mask processing. It provides guidelines to enhance the quality and consistency of a condition assessment approach, as well as, reducing the cost and time required to inspect subway networks. The proposed methodology has been tested on segments of the Montreal subway system.
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