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.

配水系统内的内部管道腐蚀会导致氧化铁沉积在管壁上，从而可能污染供水。饮用被氧化铁污染的水会导致严重的健康问题，如胃肠道感染、皮肤病和淋巴结并发症。因此，对管道腐蚀进行非破坏性持续监测对于水资源可持续发展至关重要。本研究介绍了一种利用先进超声波技术和卷积神经网络 (CNN) 来量化管道腐蚀的双模式方法。扫描声学显微镜 (SAM) 采用高频超声波生成管道厚度的高分辨率图像，表明氧化铁堆积。 SAM 还捕获内部管道数据以测量水中的氧化铁浓度。该数据经 CNN 分析，准确率高达 95%，令人印象深刻。该双模式系统可有效评估管道腐蚀程度和水污染程度，体现了 SAM 和 CNN 成功集成以实现精确可靠的监测。