Researchers at CU Boulder are developing an app that could collect and analyse sample images of concrete for possible defects using machine learning techniques based on composition, fault lines, and visual clues.
The research is funded by a seed grant from the Engineering Education and AI-Augmented Learning Interdisciplinary Research Theme within the College of Engineering and Applied Science.
Assistant Professor Mija Hubler of civil, environmental and architectural engineering, explained: “Traditionally, we have to send samples to the lab where it is then destroyed to be analysed – so it isn’t a very efficient process in many ways.
“We are hoping to develop something where you could cut a sample open on site, take a picture and understand how that batch will perform mechanically.”
Up-skilling
According to Hubler, users of the app would need at least some basic education about machine learning to understand the inherent uncertainty in the predictions and how to proceed with them.
To address those kinds of questions, Hubler is working with computer science Assistant Teaching Professor Geena Kim.
Kim said that the scale and size of data sets in civil engineering make for interesting challenges when creating the needed algorithms, testing with students, and understanding the results for broad applications.
She added: “We need to get more data and observations to really understand how people will interact with this app and what their personal experience with AI and machine learning needs to be to use it properly.
“This work will also help with our understanding of these concepts in curriculum and workforce development over time.”
Hubler said the team will continue to refine their approach, while also seeking collaborators at CU Boulder and beyond.
Hubler concluded: “The primary way we assess and track our infrastructure in America is through visual inspections, so this kind of tool would be quite powerful.”
Image credit: Nordroden/Shutterstock
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