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<p>Sewer systems play a crucial role in protecting public health and mitigating flood risk. This study proposes a framework that integrates survival analysis and spatial data management to predict sewer failure time. Taking Hong Kong as an evidence study, comprehensive sewer data are incorporated into the ArcGIS database. The methodology employs Kaplan–Meier analysis to determine a critical time threshold (T<sub>0</sub>) at a 95% survival probability. Group differences are assessed using log-rank tests, and cumulative hazard rates are estimated via Nelson–Aalen estimation. The study investigates post-T<sub>0</sub> degradation patterns in physical, functional, and environmental factors. Based on cumulative hazard rates after T<sub>0</sub>, a tertile-based classification system defines two boundaries (T<sub>1</sub> and T<sub>2</sub>). This system categorizes pipelines into four risk levels, enabling decision-makers to select an appropriate failure time. The results are visualized through GIS mapping and supported by an iterative forecasting system that optimizes maintenance strategies through operational feedback.</p>

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Survival analysis framework for sewer failure time: evidence from Hong Kong

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月30日

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  • Jingchao Yang,
  • Dramani Arimiyaw,
  • Tarek Zayed,
  • Mohamed Nashat,
  • Xianyang Liu,
  • Abdelazim Ibrahim

摘要

Sewer systems play a crucial role in protecting public health and mitigating flood risk. This study proposes a framework that integrates survival analysis and spatial data management to predict sewer failure time. Taking Hong Kong as an evidence study, comprehensive sewer data are incorporated into the ArcGIS database. The methodology employs Kaplan–Meier analysis to determine a critical time threshold (T0) at a 95% survival probability. Group differences are assessed using log-rank tests, and cumulative hazard rates are estimated via Nelson–Aalen estimation. The study investigates post-T0 degradation patterns in physical, functional, and environmental factors. Based on cumulative hazard rates after T0, a tertile-based classification system defines two boundaries (T1 and T2). This system categorizes pipelines into four risk levels, enabling decision-makers to select an appropriate failure time. The results are visualized through GIS mapping and supported by an iterative forecasting system that optimizes maintenance strategies through operational feedback.