Internal pipe corrosion in water distribution systems causes iron oxide deposits, contaminating water and posing health risks. This study presents a dual-mode method using ultrasound and convolutional neural networks (CNNs) to quantify pipe corrosion. Scanning acoustic microscopy (SAM) generates high-resolution images of pipe thickness and measures iron oxide concentration in water. CNN analysis of SAM data achieves 95% accuracy in assessing corrosion extent and water contamination.
Publisher
npj Clean Water
Published On
Jul 13, 2024
Authors
Yeongho Sung, Hyeon-Ju Jeon, Daehun Kim, Min-Seo Kim, Jaeyeop Choi, Hwan Ryul Jo, Junghwan Oh, O-Joun Lee, Hae Gyun Lim
Tags
internal pipe corrosion
ultrasound
convolutional neural networks
water distribution systems
iron oxide deposits
health risks
contamination assessment
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