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<p>Water main breaks remain a persistent challenge to the reliability and sustainability of urban water distribution networks, yet accurate prediction is difficult because of interacting effects of infrastructure aging, environmental conditions, and operational factors. This review presents a structured synthesis of pipe break prediction models, including simplistic, physical-based, deterministic, probabilistic, and machine learning approaches. Unlike prior studies that emphasize individual techniques, the review compares these model families within a unified analytical framework, focusing on data requirements, interpretability, scalability, and practical utility for water utilities. Key predictive variables such as pipe age, diameter, and temperature are examined together with additional factors including soil properties, seasonal climate variability, and operational parameters such as hydraulic pressure. The review also discusses recurring challenges related to limited data availability, inconsistent records, and measurement uncertainty. Results highlight fundamental trade-offs among model transparency, data demand, and predictive performance. Machine learning approaches can capture complex nonlinear relationships when large datasets are available, but their implementation often requires substantial data infrastructure and careful validation. Physical and deterministic models provide clearer interpretability but may oversimplify system dynamics, while probabilistic models explicitly represent uncertainty yet require careful parameterization and calibration. Based on this synthesis, several research gaps are identified, including limited integration of environmental and operational datasets, inadequate treatment of censored or incomplete failure records, difficulties transferring models across utilities, and the absence of standardized benchmarking practices. Future research directions include hybrid physics–machine learning frameworks, improved uncertainty quantification, and standardized evaluation methods to support more reliable infrastructure maintenance planning and strategic asset management for water utilities worldwide and policymakers and practitioners.</p>

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Pipe Break Prediction in Water Distribution Systems: A State-of-the-Art Review and Future Opportunities

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Amir Noori,
  • Ehsan Roshani,
  • Hossein Bonakdari

摘要

Water main breaks remain a persistent challenge to the reliability and sustainability of urban water distribution networks, yet accurate prediction is difficult because of interacting effects of infrastructure aging, environmental conditions, and operational factors. This review presents a structured synthesis of pipe break prediction models, including simplistic, physical-based, deterministic, probabilistic, and machine learning approaches. Unlike prior studies that emphasize individual techniques, the review compares these model families within a unified analytical framework, focusing on data requirements, interpretability, scalability, and practical utility for water utilities. Key predictive variables such as pipe age, diameter, and temperature are examined together with additional factors including soil properties, seasonal climate variability, and operational parameters such as hydraulic pressure. The review also discusses recurring challenges related to limited data availability, inconsistent records, and measurement uncertainty. Results highlight fundamental trade-offs among model transparency, data demand, and predictive performance. Machine learning approaches can capture complex nonlinear relationships when large datasets are available, but their implementation often requires substantial data infrastructure and careful validation. Physical and deterministic models provide clearer interpretability but may oversimplify system dynamics, while probabilistic models explicitly represent uncertainty yet require careful parameterization and calibration. Based on this synthesis, several research gaps are identified, including limited integration of environmental and operational datasets, inadequate treatment of censored or incomplete failure records, difficulties transferring models across utilities, and the absence of standardized benchmarking practices. Future research directions include hybrid physics–machine learning frameworks, improved uncertainty quantification, and standardized evaluation methods to support more reliable infrastructure maintenance planning and strategic asset management for water utilities worldwide and policymakers and practitioners.