Editorial: Recent advances in risk and community resilience analysis against windstorms

The first article, "A novel framework to study community-level social and physical impacts of hurricaneinduced winds through synthetic scenario analysis" presents a new framework for studying the social and physical impacts of hurricane-induced winds at the community level. The authors developed a computational framework to simulate the scenario of a hurricane impacting a community. Hurricane wind field is simulated using a parametric wind field model that creates synthetic hurricane tracks based on historical data. This approach enables the estimate of gust wind speed at the location of each building which is used by a stochastic damage simulation algorithm to assess the buildings' physical damage. The framework, then, uses the buildings' physical damage to estimate the direct financial losses and social impacts (e.g., household dislocation, employment disruption, and education disruption). The framework is applied to the community of Onslow County, North Carolina which develops a better understanding of the interplay between social and physical impacts.The second article, "A case study and parametric analysis of predicting hurricane-induced building damage using data-driven machine learning approach" presents a data-driven machine learning framework to predict building-level damage from future hurricanes. The framework uses exposure and hazard data as input for a classification algorithm (random forest) to categorize building vulnerability into discrete damage states (i.e., No Damage, Non-Structural Damage, and Structural Damage). The exposure data includes the building's structural, geometric, and geospatial features while the hazard data includes wind speed and water inundation. The framework is trained using available reconnaissance datasets for four hurricanes: Hurricanes Harvey (2017), Irma (2017), Michael (2018), and Laura (2020). The hindcast accuracy of the random forest algorithm is 76%. Finally, this article shows that the results from the framework outperform FEMA's Hazus Multi-Hazard Hurricane Model, which yielded 47% accuracy. This comparison offers insights into alternatives for forecasting models given the variability of rapidly available data used in the ML framework as presented.The third article, "The influence of ASCE 7-16 wind load provisions on a vulnerability model of Florida residential construction" explores the influence of the changes to the wind-load provisions in ASCE 7-16, which is adopted in the Florida Building Code, on the vulnerability of residential construction in Florida. The authors, in this study, use the vulnerability model framework within the Florida Public Hurricane Loss Projection Model. The study focuses on the development and implementation of these changes within the model and provides more insights into the effectiveness of such changes to improve community resilience.The final article, "Design of stick-framed wood roofs under tornado wind loads" focuses on the design of stick-framed wood roofs under tornado wind loads. The authors present a comprehensive study of the behavior of stick-framed wood roofs under tornado wind loads and provide design guidelines for improving the resilience of these structures. The study is conducted using a non-linear finite element model of a stick-framed roof that is designed following the guidelines in the National Building Code of Canada. The most notable findings, regarding design requirements to withstand EF-2 tornadoes, are an improved gable end frame, adding hurricane ties at all roof-to-wall connections, and increasing the number of nails at various connections.In conclusion, this research topic provides valuable insights into the risk and resilience against windstorms and highlights the importance of continued research in this area. The articles presented in this research topic demonstrate the use of innovative approaches to understanding windstorm risk and improving resilience, and provide important contributions to the field of windstorm risk and resilience analysis. We hope that this research topic will serve as a valuable resource for researchers, practitioners, and policymakers working in this area.

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