An image-based data model for subway condition assessment Dawood, Thikra; Zhu, Zhenhua; Zayed, Tarek
The Canadian Urban Transit Association (CUTA) estimated that transit infrastructure needed a total of 53 Billion Canadian Dollars in 2013. Subway networks form an essential part of the public transportation infrastructure. Several surface defects may develop on subway infrastructure facilities, of which the most commonly identified are cracks, scaling, spalling, delamination, moisture marks, and efflorescence. These distresses participate not only in degrading the structure aesthetically, but in increasing the deterioration mechanisms of its components, taking into account the severe environmental conditions and continuous heavy loads that the structure is subjected to during its service life. High deterioration rates may cause the closure of subway system, therefore condition assessment of subway networks represents a crucial yet 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 depend inherently on subjective criteria. Several models have been proposed by previous researchers to assess the condition of subway systems. However, all of the developed methods were dependent on the visual inspection reports, hence they lacked the objectivity in quantifying and estimating the severity of defects. Therefore, a robust model that can detect the distresses and compute their severity needs to be developed. This paper defines the details of the recently introduced procedure based on image processing and assessment techniques. A five phased process is presented for accurate condition assessment of subway networks. The developed methodology utilizes data acquisition tools for collecting images of different elements in subway networks. Multiple algorithms are utilized to detect, interpret and measure surface defects, such as binary transformation, histogram equalization, image dilation, and hole filling. A case study from Montreal subway system was used to exemplify the application of the developed method. The results prove the potential benefits of the proposed methodology in identifying and quantifying surface defects. This research concludes the reliability of image-based data model in terms of accuracy, efficiency, and ease of analysis.
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