目前已发展出多种基于模拟和解析的方法来评估单个结构的地震易损性。然而，社区的地震安全与韧性在很大程度上受网络可靠度的影响，而网络可靠度不仅取决于组件的易损性，还取决于网络拓扑以及物资/信息流。由于复杂的网络拓扑、地面运动之间的相互依赖性以及较低的失效概率，网络的地震可靠度分析往往面临巨大的计算挑战。本文提出了一种利用子集模拟进行网络易损性分析的方差缩减方法，以克服这些挑战。该方法将二元网络极限状态函数重构为更具信息量的分段连续函数。所提出的极限状态函数量化了每个样本到网络失效域的接近程度，从而能够构建专门的中间失效事件，这些事件可用于子集模拟及其他序贯蒙特卡洛方法。此外，通过识别中间失效事件与地震烈度之间的隐含关系，我们提出了一种技术，仅需执行一次专门的子集模拟即可获得完整的网络易损性曲线。数值算例表明，该方法能够有效评估大规模网络的系统级易损性。

Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, the seismic safety and resilience of a community are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. Seismic reliability analyses of networks often encounter significant computational challenges due to complex network topologies, interdependencies among ground motions, and low failure probabilities. This paper proposes a variance-reduction method for network fragility analysis using subset simulation to overcome these challenges. The binary network limit-state function is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to the network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches. Moreover, by identifying an implicit relationship between intermediate failure events and seismic intensity, we propose a technique to obtain the entire network fragility curve with a single execution of specialized subset simulation. Numerical examples demonstrate that the proposed method can effectively evaluate system-level fragility for large-scale networks.