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  1. Free, publicly-accessible full text available September 1, 2024
  2. Fabio Biondini, Dan M. (Ed.)
    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. 
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    Free, publicly-accessible full text available June 27, 2024
  3. Free, publicly-accessible full text available May 1, 2024
  4. null (Ed.)
    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. 
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  5. null (Ed.)