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<p>Large-diameter gravity aqueducts are essential for water supply systems but face performance and safety risks from complex flow conditions. Effective flow-state monitoring is critical for hydraulic performance and infrastructure safety. However, conventional monitoring techniques like closed-circuit television (CCTV) inspection and ultrasonic sensing have limited real-time accuracy in distinguishing flow states. Here we show a real-time, distributed flow monitoring framework based on distributed acoustic sensing (DAS). A hierarchical clustering model, called DAS-Hydro HierarchyNet, was developed to analyze low-frequency acoustic signals and classify water flow states using a multi-level approach. The framework enables continuous flow monitoring along large aqueducts, overcoming point-based measurement limits. A 6 km case study in the Pearl River Delta demonstrates this approach’s feasibility and effectiveness. The results confirm that DAS combined with advanced AI classification enables accurate flow-state monitoring, water location detection, and flow velocity estimation, offering a scalable, intelligent solution for large-scale transmission monitoring.</p>

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Real-time monitoring of water states in large-diameter aqueducts – learning from distributed acoustic sensing signals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月16日

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  • Dao-Yuan Tan,
  • Zhen-Yu Tang,
  • Zhen-Rui Yan,
  • Jing Wang,
  • Wei Zhang,
  • Jing-Wu Huang,
  • Peng Wang,
  • Zhiguo Yuan,
  • Huan-Feng Duan,
  • Bin Shi,
  • Hong-Hu Zhu

摘要

Large-diameter gravity aqueducts are essential for water supply systems but face performance and safety risks from complex flow conditions. Effective flow-state monitoring is critical for hydraulic performance and infrastructure safety. However, conventional monitoring techniques like closed-circuit television (CCTV) inspection and ultrasonic sensing have limited real-time accuracy in distinguishing flow states. Here we show a real-time, distributed flow monitoring framework based on distributed acoustic sensing (DAS). A hierarchical clustering model, called DAS-Hydro HierarchyNet, was developed to analyze low-frequency acoustic signals and classify water flow states using a multi-level approach. The framework enables continuous flow monitoring along large aqueducts, overcoming point-based measurement limits. A 6 km case study in the Pearl River Delta demonstrates this approach’s feasibility and effectiveness. The results confirm that DAS combined with advanced AI classification enables accurate flow-state monitoring, water location detection, and flow velocity estimation, offering a scalable, intelligent solution for large-scale transmission monitoring.