Abstract：[Objective] Seismic damage and destruction of the stations, tunnels, and other structures considerably impair the functionality of the urban rail transit system. Current research on the system performance of the rail transit network primarily focuses on the scenarios of intentional attack and stochastic damage, which is dramatically different from the earthquake disaster scenarios. This paper proposed a quantitative framework to evaluate the seismic performance and resilience of rail transit networks. [Methods] The seismic fragility model was used to calculate the failure probability of the primary structural elements, including stations, tunnels, and bridges of the rail transit system. The graphical model of the network was established using the Space L modeling method. This approach was used to depict the interdependency of system elements. The network performance was expressed by the network efficiency weighted by passenger flow between rail transit stations. The Monte Carlo simulation was used to assess the uncertainty of the earthquake damage state of structures and the post-earthquake recovery of the damaged elements. According to the network performance curves during the post-earthquake recovery process, the seismic resilience index and resilience loss of the rail transit network were quantitatively evaluated using the concept of resilience triangle. Considering the Beijing rail transit network, the effects of earthquake intensity, recovery strategy on network performance, and resilience indexes were investigated. [Results] The results of the present analysis were as follows. (1) The resilience characteristics of rail transit networks under earthquakes, intentional attacks, and stochastic damage were different. The resilience index under earthquake damage was 0.936 3, whereas the resilience index under stochastic damage was 0.934 0. The resilience index under intentional attack was 0.863 4. (2) In the damage scenario corresponding to different earthquake intensities, the system resilience index calculated by the recovery sequence sorted by the dynamic importance of damaged elements were larger than that sorted by the static importance of damaged elements. Moreover, the damage scenario involving several damaged elements typically results in a larger difference between the resilience index calculated by the two recovery strategies. (3) Pre-earthquake enhancement measures to reduce the failure probability of crucial elements could effectively enhance the disaster resistance capacity of the network; however, their influence on improving the post-earthquake recovery capacity remained unclear. [Conclusions] Based on the seismic fragility models of the primary structure of the rail transit network, the graphical model of the network, and the importance of ranking-based post-earthquake recovery of the damaged elements, this paper establishes a framework to quantitatively evaluate the seismic resilience of rail transit network by the passenger-weighted network efficiency. When evaluating network resilience and comparing antiseismic improvement measures, multiple indicators such as resilience index, resilience loss, and recovery duration should be comprehensively analyzed. This framework can provide a reference for the seismic performance evaluation of the urban rail transit network and help decision-makers in allocating maintenance resources to restore the operation function of the urban rail transit system in a timely and cost-effective manner during the recovery process.
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2024, Vol. 64 
