Bridge inspections are time-consuming, inefficient and often subjective processes that rely heavily on human judgment, leading to overlooked or underestimated structural problems or damage. A new process developed by Arvin Ebrahimkhanlou, PhD, assistant professor of civil, architectural and environmental engineering at Drexel University, aims to address this issue.
Using graph theory algorithms, a system converts images of concrete cracks into mathematical graph representations with features like assigned values for the intersecting cracks. Machine learning algorithms then correlate these graphed crack features to different damage levels based on the crack sample data.
“Creating a mathematical representation of cracking patterns is a novel idea and the key contribution of our recent paper,” Ebrahimkhanlou said. “We find this to be a highly effective way to quantify changes in the patterns of cracking, which enables us to connect the visual appearance of a crack to the level of structural damage in a way that is quantifiable and can be consistently repeated regardless of who is doing the inspection.”
In tests, the AI correctly assessed over 90% of cracked samples’ damage levels. This rapid quantification method could identify problems even before human inspectors notice them visually. By leveraging AI to impose order on chaotic visual features, this innovation aims to pinpoint infrastructure problems before catastrophic failures occur.