Corrosion is natural, and it’s everywhere. While many techniques can be applied to stave off corrosion, nothing lasts forever when exposed to the elements of nature, so consistent and planned evaluation is essential to keep the things we build working and safe. A research team at the University of Illinois (U. of I.) led by Shengyi Wang, a Ph.D. candidate in the Department of Civil and Environmental Engineering, is using NCSA resources to help streamline the process of evaluating infrastructure for corrosion.
You probably have a bridge in your town, and you certainly have pipelines and water systems. Your city is one of tens of thousands of cities across the country – every one with infrastructure that needs to be maintained. Maintenance is costly, especially when considering how many pieces of infrastructure there are.
“Corrosion poses significant challenges to various infrastructure assets,” Wang said, “including bridges, pipelines, military equipment and water systems. It can lead to safety hazards, substantial economic losses and environmental risks. Notably, the United States allocates 40% of its National Maintenance Budget to corrosion-related repairs.”
Evaluating these various infrastructures takes a great deal of time and expertise. “Corrosion is a major issue affecting the durability and safety of critical infrastructure, leading to significant maintenance costs and safety risks,” said Wang. “Traditional corrosion assessment methods are manual, subjective, and time-consuming, requiring human inspectors to measure corrosion areas.”
It takes an expert to find corrosion, especially in its earliest stages. Something critical and subtle could be happening in the supports of a bridge, for example, that only a trained specialist could find. With as many pieces of infrastructure in the U.S. that people use every day, it’s challenging to keep up with demand. Wang’s research is a critical step in alleviating some of these issues.
“My research aims to automate and enhance corrosion detection, segmentation and measurement using a deep learning-based image segmentation model, which can improve accuracy, efficiency and consistency in corrosion analysis,” said Wang.
Wang’s research involves training an AI using images with and without labels. The idea is that you give some guidance to the AI, in this case, pictures of corrosion that human experts label, and then allow the AI to learn by example how to detect corrosion in unlabeled pictures. This method is called CNN-based semi-supervised learning (SSL). Wang further explains how he’s using this method in his research. Read the full article on the NCSA website.