## 2 Related Research
### Transportation Network Resilience Assessment  
In recent years, resilience has garnered significant interest from researchers and practitioners across various disciplines. Current transportation resilience research primarily falls into two categories: pre-disaster infrastructure risk warning and post-disaster transportation network functionality assessment. The former utilizes multi-source monitoring technologies (e.g., Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR) [25] and Unmanned Aerial Vehicle (UAV) [26]) to quantify infrastructure failure probabilities and facilitate long-term geological disaster risk warnings. The latter focuses on the immediate impact of disaster events on road network connectivity and accessibility, as well as their recovery capabilities [27; 28]. This study aligns with the latter approach, aiming to develop a quantitative model and optimization framework for post-disaster dynamic resilience assessment. As shown in Table 1, existing studies on transportation resilience primarily adopt two mainstream perspectives. The first defines resilience as an inherent capability or attribute of a system, emphasizing its ability to maintain functionality during disruptive events [29]. The second, though less common, conceptualizes resilience as a quantifiable dynamic function of system performance, used to assess network behavior under potential disruptions [19]. Despite these differing perspectives, most definitions converge on two core dimensions: a transportation network's ability to withstand disruptions and its capacity for rapid recovery following failures. The overarching goal remains to characterize the evolution of system performance under disruptive events. System resilience is a multi-dimensional and dynamic concept commonly used to evaluate a system's ability to absorb external disturbances and recover [20; 30]. Most studies measure resilience by assessing whether a system's performance or functionality meets the required level before and after a disturbance event [31]. In terms of methods for assessing transportation system resilience, [PERSON] [37] introduced the resilience triangle, which measures resilience based on the magnitude of anticipated quality decline (failure probability) over time (i.e., recovery time). However, this approach assumes a linear recovery phase connecting pre- and post-disaster performance, thus lacking a detailed model of the recovery phase. In reality, road networks in extreme environments prone to frequent disasters may experience extended recovery periods [38]. They developed a comprehensive resilience assessment framework based on the structural and functional performance of road networks, using the \"R4 Framework\" to analyze system resilience attributes. [PERSON] et al. [20] proposed a dynamic resilience assessment framework for highway networks under extreme conditions from a long-term operational perspective. However, current resilience quantification models typically address only one disturbance event at a time, while disaster-prone areas often face simultaneous events affecting multiple components of the system. Therefore, it is necessary to consider multiple types of disturbances simultaneously, as noted in [PERSON] and [PERSON] [39].