An expert-data fusion framework for uncertainty-aware resilience assessment of urban metro networks under flooding

Urban metro networks are increasingly vulnerable to flood-induced disruptions, necessitating resilience assessment frameworks that explicitly account for uncertainty while integrating both expert judgment and empirical evidence. This study develops an uncertainty-aware expert-data fusion framework for multi-stage resilience assessment of metro networks, structured along the Prepare–React–Recover–Adapt cycle. Using 23 indicators spanning natural, social and operational dimensions, the framework captures the stage-specific resilience characteristics across flood-management phases. Methodologically, the framework integrates hybrid weighting and uncertainty-aware evaluation within a unified architecture. The Comprehensive Weight Determination (CWD) scheme combines entropy-based weighting, causal interaction modeling and Pythagorean fuzzy hierarchical analysis to jointly represent data variability, indicator interdependencies and expert-informed importance. This is further coupled with a Cloud Matter-Element (CME) model, which incorporates probabilistic–fuzzy representation to account for both stochastic and epistemic uncertainties. Application to the Shanghai metro network demonstrates the practical utility of the framework for stage-specific, uncertainty-aware resilience classification. Under a 50% data-disturbance scenario, weight variability is reduced by 59.8% compared with conventional methods. The results reveal pronounced spatial heterogeneity, with higher resilience in central districts and persistent vulnerabilities in peripheral corridors. Causal analysis indicates that resilience is governed by the interaction between demand pressure and infrastructural capacity, with population density as a key driver and station load capacity as a critical bottleneck. These findings highlight the capability of the proposed framework to provide interpretable and uncertainty-aware support for resilience-oriented decision-making, enabling targeted intervention and stage-adaptive management in flood-prone urban metro networks.