Some initiatives that are intended to mitigate extreme flood events do not fully consider the impact of less catastrophic but more commonly occurring wind-induced damage, which is a significant issue particularly in regions that are prone to hurricanes. The policies of the U.S. Federal Emergency Management Agency (FEMA) encourage homeowners to implement permanent static elevation (PSE) to increase their flood resilience. However, substantial elevation can increase a structure’s vulnerability to wind. In effect, by protecting against a rare but catastrophic flood occurrence, these houses are made considerably more vulnerable to less severe but more regularly occurring wind events and thus face an increased likelihood of wind damage. This study introduces amphibious construction as an innovative retrofit flood-mitigation and climate-change-adaptation strategy. It also evaluates the increased vulnerability to wind damage that accompanies PSE, to which amphibious retrofit construction is an alternative. The results of our investigation suggest that amphibious construction could provide a beneficial alternative solution to mitigating hurricane damage because it is a strategy that can reduce vulnerability to flood damage without increasing vulnerability to wind damage.

Widespread destruction from large-scale disasters results in lengthy and costly recovery of damaged infrastructure and communities, with societal and economic impacts persisting many years after a hazard event. Robust and coherent reconstruction planning and rapid execution play pivotal roles in ameliorating time-consuming postdisaster recovery. Reconstruction generally constitutes a considerable portion of an entire disaster recovery process that requires significant resources, including materials, workforce, funding, time, and others. To pursue maximum socioeconomic benefits, which can be one of the critical goals of the postdisaster recovery process, optimized recovery strategies should be designed by considering given federal, state, and local resources. Even though recent studies have proposed disaster recovery planning and execution models, they rarely addressed social and economic indicators required for comprehensive postdisaster recovery plans. This paper presents a new postdisaster recovery prioritization model, including social and economic factors, that evaluates priorities of damaged facilities recovery projects in affected regions through a resource allocation analysis. The primary goal of the proposed model is to optimize resource allocation and quantify and maximize socioeconomic benefits for a postdisaster reconstruction project portfolio through multicriteria decision analysis and multiobjective optimization. Also, this research study adopts a modular construction technique as a potential solution for facilitating the recovery processes, which can significantly reduce their time and cost. The proposed model is expected to provide a comprehensive decision-making process for researchers and practitioners to design a postdisaster recovery plan and arrange their available resources by considering the socioeconomic factors of affected communities.

Windstorms result in significant damage and economic loss and are a major recurring threat in many countries. Estimating surface-level wind speeds resulting from windstorms is a complicated problem, but geostatistical spatial interpolation methods present a potential solution. Maximum sustained and peak gust weather station data from two historic windstorms in Europe were analyzed to predict surface-level wind speed surfaces across a large and topographically varied landscape. Disjunctively sampled maximum sustained wind speeds were adjusted to represent equivalent continuously sampled 10-minute wind speeds and missing peak gust station data were estimated by applying a gust factor to the recorded maximum sustained wind speeds. Wind surfaces were estimated based on anisotropic and isotropic kriging interpolation methodologies. The study found that anisotropic kriging is well-suited for interpolating wind speeds in meso- and macro-scale areas because it accounts for wind direction and trends in wind speeds across a large, heterogeneous surface, and resulted in interpolation surface improvement in most models evaluated. Statistical testing of interpolation error for stations stratified by geographic classification revealed that stations in coastal and/or mountainous locations had significantly higher prediction errors when compared with stations in non-coastal/non-mountainous locations. These results may assist in mitigating losses to structures due to excessive wind events.