## 1 Introduction
ity of community resilience, particularly in the context of flooding, remains a formidable challenge. The intricate, non-linear interactions among variables, the integration of diverse data sources, and the lack of standardized mathematical models add layers of complexity to resilience assessments. Additionally, critical data gaps and the absence of universally applicable frameworks further hinder the accurate quantification and evaluation of resilience [17]. These challenges highlight the necessity for innovative approaches that address the multifaceted nature of community resilience while accommodating the unique characteristics of flooding events. Various models and frameworks have been proposed to evaluate and quantify resilience, addressing many of the challenges inherent in community-level resilience assessment. Among these are simulation-based approaches, which model system behaviors under different scenarios [9; 18], probabilistic methods that quantify uncertainties and predict outcomes [19; 20], and fuzzy logic methods, which handle imprecision and ambiguity in resilience evaluations [21]. In addition to these approaches, fragility analysis has emerged as a reliable and valuable tool for resilience-based decision-making, offering planners and stakeholders actionable insights. For instance, ref. [16] introduced the concept of the building portfolio fragility function, defined as the probability that a building portfolio fails to achieve specified performance objectives under a given earthquake scenario. They demonstrated its application using the Centerville community to illustrate how this approach characterizes building vulnerability. Similarly, ref. [22] underscored the importance of developing appropriate fragility models to advance performance-based engineering. They proposed a methodology to assess the probable response of light-frame residential infrastructure exposed to natural and human-made hazard levels. In another example, the hurricane and earthquake models in HAZUS incorporate fragility functions to describe the vulnerability of buildings, further validating the utility of this approach in resilience assessments [23]. Building on this foundation, the proposed method in this study employs flooding-specific fragilities and integrates functional interdependence to establish a connection between community-level resilience, the functionality of key building sectors, and individual building archetypes. The primary objective of this investigation is to rigorously evaluate and quantify community resilience in the context of flood-related events. To achieve this, the proposed methodological framework is constructed upon three core components. First, discrete building fragility curves are formulated using principles from performance-based engineering, drawing parallels with established methodologies for seismic assessment. Second, fragility curves for essential building sectors are developed at the sub-assembly level to capture their contribution to the overall functionality of the community fabric. Third, these efforts are synthesized into community-wide functionality fragility curves, providing a holistic measure of resilience at the macroscopic level. Fragility curves for supporting building sectors are integrated with an interaction matrix within a community-level model to evaluate residual functionality, enabling more effective risk-informed decision-making and response strategies. This proposed method represents a significant advancement in quantitative community-level resilience analysis by addressing the unique challenges posed by flood events. While innovative in its application to flooding resilience, this approach can also be adapted to incorporate additional community sectors, ensuring flexibility and broader applicability. By bridging the gap between traditional fragility analyses and community-wide functionality assessments, this framework aims to enhance resilience planning and facilitate the development of better-informed strategies for mitigating flood-related risks.