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Internal pipe corrosion assessment method in water distribution system using ultrasound and convolutional neural networks

Engineering and Technology

Internal pipe corrosion assessment method in water distribution system using ultrasound and convolutional neural networks

Y. Sung, H. Jeon, et al.

Explore groundbreaking research conducted by Yeongho Sung, Hyeon-Ju Jeon, Daehun Kim, Min-Seo Kim, Jaeyeop Choi, Hwan Ryul Jo, Junghwan Oh, O-Joun Lee, and Hae Gyun Lim on internal pipe corrosion in water distribution systems. This innovative study combines ultrasound and convolutional neural networks to accurately quantify corrosion and assess water safety, ensuring healthier drinking water for all.

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Playback language: English
Abstract
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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