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Purpose <p>Urban water distribution networks (UWDNs) face increasingly critical resilience challenges due to aging infrastructure (service life exceeding 30&#xa0;years) and intensifying environmental stressors. Existing assessment frameworks often rely on single-dimensional indicators and lack quantitative mechanisms for addressing ambiguous factors. This study establishes a comprehensive, multi-criteria resilience assessment system to identify key barriers and propose data-driven optimization strategies.</p> Methodology <p>An integrated Analytic Hierarchy Process (AHP) and Fuzzy AHP (FAHP) approach was applied within a three-tier framework comprising five criteria and twenty indicators. Data were obtained from 18 interdisciplinary experts representing diverse Chinese UWDN contexts (60% eastern coastal, 40% central/western) to ensure geographical representativeness. Validation procedures included three Delphi rounds, consistency checks (CR &lt; 0.1), ±30% sensitivity analysis, and software cross-validation using MATLAB and Yaahp.</p> Results <p>Physical infrastructure (weight = 0.290) and environmental stressors (weight = 0.282) were identified as dominant resilience dimensions, jointly explaining 57.2% of total variation. Pipeline aging (12.10%) and soil corrosivity (10.31%) emerged as primary barriers, with the top ten factors accounting for 73.6% of the overall weight. The composite resilience score reached 3.59, corresponding to a “Moderately Consistent” resilience level.</p> Significance <p>The proposed AHP–FAHP hybrid framework bridges methodological gaps in multi-criteria UWDN resilience quantification. It supports evidence-based prioritization for interventions such as pipeline renewal, anti-corrosion strategies, and intelligent monitoring deployment—shifting urban water management from reactive maintenance toward proactive resilience enhancement. Future research should incorporate dynamic monitoring data to improve temporal adaptability and predictive capability.</p>

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Multi-criteria resilience assessment and critical barrier analysis of urban water distribution networks based on AHP–FAHP

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Qiang Li,
  • Zijing Wu

摘要

Purpose

Urban water distribution networks (UWDNs) face increasingly critical resilience challenges due to aging infrastructure (service life exceeding 30 years) and intensifying environmental stressors. Existing assessment frameworks often rely on single-dimensional indicators and lack quantitative mechanisms for addressing ambiguous factors. This study establishes a comprehensive, multi-criteria resilience assessment system to identify key barriers and propose data-driven optimization strategies.

Methodology

An integrated Analytic Hierarchy Process (AHP) and Fuzzy AHP (FAHP) approach was applied within a three-tier framework comprising five criteria and twenty indicators. Data were obtained from 18 interdisciplinary experts representing diverse Chinese UWDN contexts (60% eastern coastal, 40% central/western) to ensure geographical representativeness. Validation procedures included three Delphi rounds, consistency checks (CR < 0.1), ±30% sensitivity analysis, and software cross-validation using MATLAB and Yaahp.

Results

Physical infrastructure (weight = 0.290) and environmental stressors (weight = 0.282) were identified as dominant resilience dimensions, jointly explaining 57.2% of total variation. Pipeline aging (12.10%) and soil corrosivity (10.31%) emerged as primary barriers, with the top ten factors accounting for 73.6% of the overall weight. The composite resilience score reached 3.59, corresponding to a “Moderately Consistent” resilience level.

Significance

The proposed AHP–FAHP hybrid framework bridges methodological gaps in multi-criteria UWDN resilience quantification. It supports evidence-based prioritization for interventions such as pipeline renewal, anti-corrosion strategies, and intelligent monitoring deployment—shifting urban water management from reactive maintenance toward proactive resilience enhancement. Future research should incorporate dynamic monitoring data to improve temporal adaptability and predictive capability.