Urban infrastructure managers and civil engineers now have a better tool to detect and evaluate structural distress in subway networks, thanks to Concordia’s Department of Building, Civil and Environmental Engineering (BCEE).
PHD candidate Thikra Dawood, in collaboration with professor Tarek Zayed and assistant professor Zhenhua Zhu, has developed an integrated model for the detection and quantification of spalling, a significant surface defect in concrete infrastructure. Left untreated, it can compromise integrity and lead to collapse. Their new research was featured in an article recently published in Automation in Construction.
The model integrates digital photography, image-processing techniques and computer-based learning in a largely automated process. It is being touted as a more objective and consistent substitute for the visual techniques typically used by inspectors to assess spalling distress on-site.
Research has found the latter methods to be time-consuming, costly, labour-intensive and error-prone.
Subjective distress
Spalling is typically caused by water infiltration and can be identified by the telltale pitting of concrete surfaces. The depth of the pitting correlates with the severity of the flaw in the structure, but the severity is difficult to diagnose by visual means alone.
“Visual inspection techniques are the most common form of identifying spalling distress, but they’re not reliable in terms of accuracy,” says Dawood, the article’s lead author.
“If you send two inspectors to the same site, they’re liable to produce divergent measurements of the distress. This is because their methods are subjective, based on their knowledge and experience. What’s more, some places are inaccessible to the inspectors, and they’re forced to rely on estimates.”
In place of visual techniques, Dawood’s integrated model proposes a simple three-step process: first, an image of the spalling defect is taken with a digital camera. Then the image is transferred to a computer, where it is treated with image-processing algorithms and filters to remove the noise within its frame and separate the defect from the background of the image.
Finally, the enhanced information detected within the image is channeled to a regression model and developed using a programming language known as MATLAB.
“At this stage, we can determine the length and width of the defect, which in turn can be used to create a realistic 3D model of the spalling defect.”