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<p>Wastewater transport and recycling are major issues in the context of global pollution and climate change. In particular, drainage pipe networks are critical urban infrastructures for sewage transportation and flood drainage, yet these pipes are susceptible to structural and functional defects that compromise their integrity and efficiency.&#xa0;Failure in drainage pipelines can lead to catastrophic consequences, including urban flooding, soil contamination, sinkholes, and significant economic losses.&#xa0;Here, we review advanced methods to monitor structural and functional defects of drainage pipe networks. We present common defects of drainage pipe networks. Defect detection methods include software-based methods, acoustic methods, infrared thermography, smart ball systems, ground-penetrating radar, and distributed fiber-optic sensing. We found that integrating multi-sensor fusion, software-hardware synergy, and artificial intelligence significantly improves defect detection accuracy and predictive maintenance. We provide insights for selecting optimal monitoring strategies by comparing the principles, applications, and performance of the&#xa0;detection methods.</p>

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Methods to monitor the defects of the drainage pipe network: a review

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Boyuan Xue,
  • Eric Lichtfouse,
  • Xiaohong Zhou

摘要

Wastewater transport and recycling are major issues in the context of global pollution and climate change. In particular, drainage pipe networks are critical urban infrastructures for sewage transportation and flood drainage, yet these pipes are susceptible to structural and functional defects that compromise their integrity and efficiency. Failure in drainage pipelines can lead to catastrophic consequences, including urban flooding, soil contamination, sinkholes, and significant economic losses. Here, we review advanced methods to monitor structural and functional defects of drainage pipe networks. We present common defects of drainage pipe networks. Defect detection methods include software-based methods, acoustic methods, infrared thermography, smart ball systems, ground-penetrating radar, and distributed fiber-optic sensing. We found that integrating multi-sensor fusion, software-hardware synergy, and artificial intelligence significantly improves defect detection accuracy and predictive maintenance. We provide insights for selecting optimal monitoring strategies by comparing the principles, applications, and performance of the detection methods.