A resilience evolution model of urban lifeline systems during operation based on performance state transitions

To better understand the resilience evolution dynamics of urban lifeline systems over extended operational periods, this study introduces a model inspired by the susceptible-infected-recovered (SIR) model, which is traditionally used to simulate population health transitions. By analyzing the mechanisms governing the performance state evolution of urban lifeline systems under disaster scenarios, integrating a disaster scenario model with resilience assessment methodologies, and comprehensively considering three key resilience components—resistance, recovery, and adaptability—we develop a system dynamics resilience‒reliability (SDR-R) model. A hypothetical case study is conducted to validate the model’s applicability. The results indicate that the interplay of resistance, recovery, and adaptability influences the dynamic evolution of system performance across three states: disability performance, survivability performance, and recovery performance. The model reveals a cyclical pattern in resilience enhancement, with adaptability emerging as a critical determinant. Moreover, the SDR-R model not only simulates urban lifeline performance state evolution under single disaster scenarios but also captures resilience evolution trends over long-term system operations. The case study findings reveal that resilience decreases as disaster severity intensifies, yet positive feedback from adaptability fosters resilience improvement over time. The process of resilience evolution can be divided into four distinct phases: initial impact, adaptive priming, adaptive enhancement, and threshold effect. Notably, resilience dynamics vary significantly across disaster levels. While systems exhibit high resilience under low-level disasters, resilience gradually stabilizes at a high level in medium- and high-level disaster scenarios. However, extreme disasters introduce greater fluctuations in resilience, underscoring the necessity for targeted resilience-enhancing strategies. The insights derived from this study offer methodological guidance for understanding urban lifeline resilience evolution and developing strategies to enhance system robustness.