Uncovering Complex Dam Failure Consequences: A Hybrid Gray–Fuzzy Assessment Framework for Hazard-Prone Reservoirs

Accurately assessing the consequences of dam failure is a critical yet challenging task in modern dam risk management, especially for aging dams under increasingly extreme climates. Traditional assessment methods often struggle to handle the dual uncertainties—inherent fuzziness in expert judgments and grayness in limited data—leading to potentially biased risk rankings. To address this gap, this study proposes a novel hybrid assessment framework. The framework first employs a hybrid weighting method based on the criteria importance through intercriteria correlation (CRITIC) approach to balance subjective and objective information, ensuring a rational weight allocation for evaluation indicators. Subsequently, it integrates gray clustering (GC) and variable fuzzy set (VFS) theory to construct a GC-VFS fusion model, which is specifically designed to tackle the complex fuzziness and grayness in risk systems. The proposed framework was validated through case studies of five high-risk dams in Jiangxi Province, China. The results demonstrate that the GC-VFS model provides a more reasonable quantitative ranking and classification of dam failure consequences compared to using GC or VFS methods alone. It overcomes the limitations of several conventional models by providing more nuanced risk level classifications, and its feasibility is verified by consistency with actual risk profiles. This research offers a robust tool for dam safety management, providing valuable insights for prioritizing risk mitigation efforts in data-scarce environments.