Abstract：
Resilience assessment for urban drainage systems is a fundamental aspect of building resilient cities. Recently, some scholars have proposed the Global Resilience Analysis (GRA) method, which assesses resilience based on the functional performance of different system failure scenarios. Compared to traditional system dynamics methods, the GRA method considers the impact of internal structural failure on resilience but requires a large amount of computation. This research proposed an improved GRA method to enhance computational efficiency and practicality by reducing the number of system scenario simulations. Firstly, a hydrodynamic model of the drainage network of Haidian Island has been constructed using the Storm Water Management Model (SWMM) and Python. Secondly, the GRA method was improved using cluster analysis and convergence analysis to reduce the simulation scenarios. Thirdly, a resilience assessment index was established through system function functions, and two types of resilience enhancement measures, centralized and distributed, were proposed. The results show: (i) resilience assessment increases the computational efficiency by 25% compared to the traditional GRA method; (ii) the resilience index of the existing drainage network within Haidian Island is less than the design value (0.7) in all failure scenarios, indicating a lower level of recovery capability; (iii) compared to the centralized strategy, which is only effective when the system failure level is less than 9%, the distributed strategy enhances the resilience of the urban drainage system at a higher failure level (77%).
摘要：
城市排水系统的弹性评估是建设弹性城市的基本方面。最近,一些学者提出了全局弹性分析(GRA)方法，它根据不同系统故障场景的功能性能评估弹性。与传统的系统动力学方法相比，GRA方法考虑了内部结构失效对弹性的影响，但需要大量的计算。本研究提出了一种改进的GRA方法，通过减少系统场景仿真的数量来提高计算效率和实用性。首先,使用雨水管理模型（SWMM）和Python构建了海淀岛排水网络的水动力学模型。其次,利用聚类分析和收敛性分析对GRA方法进行了改进，以减少仿真场景。第三，通过系统功能功能建立了韧性评估指标，和两种类型的弹性增强措施，集中式和分布式，被提议。结果表明：（i）与传统的GRA方法相比，恢复力评估使计算效率提高了25％；（ii）在所有故障情况下，海淀岛内现有排水网络的恢复力指数均小于设计值（0.7），表明恢复能力水平较低；(Iii)与集中式策略相比，只有当系统故障水平低于9%时才有效，分布式策略增强了城市排水系统在较高故障级别（77％）的恢复能力。
