# Resilience Assessment of Beijing Subway Lines under Extreme Precipitation Weather
###### Abstract
identified vulnerable segments for resilience analysis of the Washington DC metro network and proposed approaches for obtaining recovery strategies considering both connectivity and total cost minimization [23]. In 2021, [PERSON] and [PERSON] provided an approach for the resilience assessment of urban transportation systems considering the effects of recovery strategies, which can address the issue of lacking recovery process modeling in traditional system resilience analyses [24]. In 2022, [PERSON] et al. proposed approaches for simplifying traffic network modeling and studied resilience recovery optimization considering recovery sequence and resource allocation [25]. More recently, [PERSON] et al. introduced robustness and vulnerability measurements for the subway network, and optimized subway system resilience under general station interruption, interchange station interruption, or traffic tunnel failure [26]. Some scholars in the field of transportation network resilience assessment have defined resilience from different perspectives. A commonality among these definitions is that they all emphasize the ability of the system to remain functional under a disruptive event, as well as the time and resources required to restore system performance. However, these transportation system resilience analyses still have the following disadvantages. First, most previous studies only focused on assessing the resilience of transportation networks under certain types of disruptive events, ignoring the critical issue of system resilience prediction before a new disruptive event. Moreover, specifically for subway systems, their resilience usually considers only one operation indicator, while the performance of the subway system is usually characterized by a set of indicators. Therefore, it is of great significance to evaluate the resilience of subway systems from the perspective of multiple indicators and provide predictions before new external disruptive events. From this perspective, this paper proposes a new method for evaluating the resilience of subway systems before new extreme rainfall events, combining the failure mode and effect analysis (FMEA) method and the Bayesian network method. In this paper, the functional structure of a subway system is decomposed, and a complete set of performance indicators is obtained. Then, by introducing the FMEA technology that has been successfully applied to reliability analysis, the possible failure modes are determined, and indicators with high-risk levels are identified. Based on the proposed resilience assessment index system and combined with meteorological monitoring data, a Bayesian network model for subway resilience assessment is established. The objectives of this work are to estimate line-level resilience under extreme weather and realize resilience assessment with multi-indicators. In this way, this work can be helpful to assist in the smart operation of Beijing's subway systems. Different from previous works using Bayesian networks to find major factors that influence system resilience, such as [PERSON] et al. (2018), this work uses Bayesian networks to characterize how extreme weather events impact system resilience and assess subway system resilience under new weather events. The structure of this paper is as follows. Section 2 proposes and constructs the subway system resilience index system and combines the FMEA analysis to determine the key resilience indicators of the subway system. Section 3 builds the resilience assessment model of a subway system based on actual data. Section 4 takes the failure data of the Changing Line of the Beijing subway in extreme rainfall weather as a case and outputs its resilience assessment results. Section 5 concludes the article.