| Autor: |
Yeongho Sung, Hyeon-Ju Jeon, Daehun Kim, Min-Seo Kim, Jaeyeop Choi, Hwan Ryul Jo, Junghwan Oh, O-Joun Lee, Hae Gyun Lim |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
npj Clean Water, Vol 7, Iss 1, Pp 1-10 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2059-7037 |
| DOI: |
10.1038/s41545-024-00358-x |
| Popis: |
Abstract Internal pipe corrosion within water distribution systems leads to iron oxide deposits on pipe walls, potentially contaminating the water supply. Consuming iron oxide-contaminated water can cause significant health issues such as gastrointestinal infections, dermatological problems, and lymph node complications. Therefore, non-destructive and continuous monitoring of pipe corrosion is imperative for water sustainability initiatives. This study introduces a dual-mode methodology utilizing advanced ultrasound technology and convolutional neural networks (CNN) to quantify pipe corrosion. Scanning acoustic microscopy (SAM) employs high-frequency ultrasound to generate high-resolution images of pipe thickness, indicating iron oxide accumulation. SAM also captures internal pipe data to measure iron oxide concentration in the water. This data, analyzed by CNN, achieves an impressive 95% accuracy. This dual-mode system effectively assesses both the extent of pipe corrosion and water contamination, exemplifying the successful integration of SAM and CNN for precise and reliable monitoring. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink