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The recent decade has seen a rise in community resilience modeling, including a quest to model infrastructure resilience (its exposure, damage, and restoration) under extreme events. These efforts entail measuring, visualizing, and probing alternatives to support mitigation, recovery, and resilience-enhancing interventions. However, the practice demands developing different input sub-models, considering various layers of uncertainty, and integrating these for the final assessment. In this project, we present how the resources of the DesignSafe Cyberinfrastructure (DesignSafe-CI) can support such efforts. We present different tools that can be leveraged from DesignSafe directly or through its interoperability with other platforms, such as the Interdependent Networked Community Resilience Modeling Environment (IN-CORE). We present illustrative examples of how to leverage publicly available data in DesignSafe-CI and models within the IN-CORE platform to create an infrastructure resilience assessment pipeline. These examples are developed and analyzed using JupyterLab in DesignSafe. Furthermore, we present how JupyterLab HPC in DesignSafe-CI enhances the modeling and testing capabilities as the analysis of larger infrastructure systems (e.g., detailed transportation networks) becomes feasible. While the illustrative example uses earthquakes as the hazard type, the leveraged tools, platforms, and shared codes can be adapted to multiple hazards. This project is created to share the material presented on December 11 (2024) in the DesignSafe Webinar "Resilience Assessment of Community Infrastructure: Leveraging HPC Resources at DesignSafe-CI and Interoperability with IN-CORE". You can find the recording of the webinar at the following link: https://youtu.be/Bdb8s4Rc4h4?feature=shared 

The recent decade has seen a rise in community resilience modeling, including a quest to model infrastructure resilience (its exposure, damage, and restoration) under extreme events. These efforts entail measuring, visualizing, and probing alternatives to support mitigation, recovery, and resilience-enhancing interventions. However, the practice demands developing different input sub-models, considering various layers of uncertainty, and integrating these for the final assessment. In this project, we present how the resources of the DesignSafe Cyberinfrastructure (DesignSafe-CI) can support such efforts. We present different tools that can be leveraged from DesignSafe directly or through its interoperability with other platforms, such as the Interdependent Networked Community Resilience Modeling Environment (IN-CORE). We present illustrative examples of how to leverage publicly available data in DesignSafe-CI and models within the IN-CORE platform to create an infrastructure resilience assessment pipeline. These examples are developed and analyzed using JupyterLab in DesignSafe. Furthermore, we present how JupyterLab HPC in DesignSafe-CI enhances the modeling and testing capabilities as the analysis of larger infrastructure systems (e.g., detailed transportation networks) becomes feasible. While the illustrative example uses earthquakes as the hazard type, the leveraged tools, platforms, and shared codes can be adapted to multiple hazards. This project is created to share the material presented on December 11 (2024) in the DesignSafe Webinar "Resilience Assessment of Community Infrastructure: Leveraging HPC Resources at DesignSafe-CI and Interoperability with IN-CORE". You can find the recording of the webinar at the following link: https://youtu.be/Bdb8s4Rc4h4?feature=shared 

Modern cities are becoming increasingly smart and interconnected, with the capacity to gather unprecedented amounts of information. However, available methods for resilience quantification lack agility to cope with the ever-changing conditions and data that underpin disaster resilience and lifecycle performance analysis. In this paper, we discuss the limitations in the models themselves, i.e. even though frameworks predict uncertain and temporally evolving system performance, they are unable to learn from new data. To address these limitations, we pose a ‘smart resilience modeling concept’ which presents the ability to update model estimations and to efficiently estimate the lifecycle resilience as new data emerges. Hypothetical examples on community infrastructure affected by deterioration effects and punctuated events are presented. This conceptualization is expected to lay a foundation for smart resilience models capable of capturing the dynamic, uncertain, and evolving characteristics of future environmental demands, societal characteristics, and infrastructure conditions. 

Although fragility function development for structures is a mature field, it has recently thrived on new algorithms propelled by machine learning (ML) methods along with heightened emphasis on functions tailored for community- to regional-scale application. This article seeks to critically assess the implications of adopting alternative traditional and emerging fragility modeling practices within seismic risk and resilience quantification to guide future analyses that span from the structure to infrastructure network scale. For example, this article probes the similarities and differences in traditional and ML techniques for demand modeling, discusses the shift from one-parameter to multiparameter fragility models, and assesses the variations in fragility outcomes via statistical distance concepts. Moreover, the previously unexplored influence of these practices on a range of performance measures (e.g. conditional probability of damage, risk of losses to individual structures, portfolio risks, and network recovery trajectories) is systematically evaluated via the posed statistical distance metrics. To this end, case studies using bridges and transportation networks are leveraged to systematically test the implications of alternative seismic fragility modeling practices. The results show that, contrary to the classically adopted archetype fragilities, parameterized ML-based models achieve similar results on individual risk metrics compared to structure-specific fragilities, promising to improve portfolio fragility definitions, deliver satisfactory risk and resilience outcomes at different scales, and pinpoint structures whose poor performance extends to the global network resilience estimates. Using flexible fragility models to depict heterogeneous portfolios is expected to support dynamic decisions that may take place at different scales, space, and time, throughout infrastructure systems.