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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 

In this project we provide downtime and resulting excess emissions data on hurricane-induced disruptions to US Gulf Coast petrochemical complexes as well as corresponding storm and facility characteristics. This data can be used in the regional risk and reliability assessment of petrochemical processing infrastructure subject to hurricane hazard-induced disruptions. This dataset might be applicable to research related to petrochemical infrastructure resilience modeling on local, regional, and global scales. 

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. 

The changing dynamics of coastal regions and climate pose severe challenges to coastal communities around the world. Effective planning of engineering projects and resilience strategies in coastal regions must not only address current conditions but also take into consideration the expected changes in the exposure and multi-hazard risk in these areas. However, existing performance-based engineering frameworks generally neglect time-varying factors and miss the opportunity to leverage related evidence as it becomes available. This paper proposes a Performance-Based Coastal Engineering (PBCE) framework that is flexible enough to accommodate uncertain time-varying factors, multi-hazard conditions, and cascading-effects. Furthermore, using a dynamic Bayesian network approach, the framework can incorporate observed evidence into the model to update the prior conditional distribution of the analyzed variables. As a proof of concept, two case studies—a typical elevated residential structure and a two-frame system—are presented, considering the effects of cascading failure, the incorporation of time-varying factors, and the influence of emerging evidence. Results show that neglecting cascading effects significantly underestimates the losses and that the incorporation of evidence reduces the uncertainty under the assumed distribution of evidence. The resulting PBCE framework can support data collection efforts, optimization of retrofitting strategies, integration of experts and community interests by facilitating interactions and knowledge sharing, as well as the identification of vulnerable regions and critical components in coastal multi-hazard regions.