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Boundary Layer Wind Tunnel (BLWT) facilities are commonly used for assessing wind loads on structures. Although BLWT facilities routinely match 1st and 2nd-order wind profile models, evidence suggests that turbulence in the roughness sublayer and the inertial sublayer exhibit non-Gaussian higher-order properties. These non-Gaussian properties can influence peak wind pressures, which govern certain structural limit states and play an important role in design. In the first part of this project, Machine learning (ML) methods are employed to identify relationships between roughness element configurations and higher-order statistical properties of the wind field. A semi-automated framework with an active learning portion and a wind tunnel experimental procedure is developed. The learning framework adaptively selects roughness profiles and launches new experiments to identify differing profiles with second-order equivalent flow as quantified by turbulence intensity. The premise is that second-order equivalent wind fields can differ in higher-order properties and therefore extreme value derived peak loads may differ. Over the course of this project, the turbulence profiles from hundreds of different Terraformer roughness element configurations were collected, providing a very rich dataset of boundary layer flow as a function of upwind fetch. Experiment 1 provides the metadata to describe and interpret measured wind profiles at the UFBLWT for a data set collected for the Benchmark experiments and 3 different phases: 1) Sinusoidal waves experiments, 2) Shape study experiments and, 3) Random field experiments. Experiment 2 of this dataset presents the results of experiments conducted in the UFBLWT, with a focus on measuring turbulence characteristics and pressure coefficients on a bluff body under varying terrain roughness configurations. The dataset provides valuable insights into the influence of upwind fetch and surface roughness on wind-induced forces, contributing to improved modeling and prediction of wind loads on structures. Based on the Terraformer configurations in experiment 1, select configurations (Benchmark and Phase 1 Terraformer configurations only) were chosen for bluff body experiments, along with additional approach turbulence measurements at a lateral location to the model. This dataset includes three key components for Benchmark and Phase 1 Terraformer configurations: reference wind velocity (uRef), lateral approach flow profiles (LatFlow), and pressure coefficients (Cpdata) on the bluff body. 

Severe natural multi-hazard events can cause damage to infrastructure and economic losses of billions of dollars. The challenges of modeling these losses include dependency between hazards, cause and sequence of loss, and lack of available data. This paper presents and explores multi-hazard loss modeling in the context of the combined wind and rain vulnerability of mid/high-rise buildings during hurricane events. A component-based probabilistic vulnerability model provides the framework to test and contrast two different approaches to treat the multi-hazards: In one, the wind and rain hazard models are both decoupled from the vulnerability model. In the other, only the wind hazard is decoupled, while the rain hazard model is embedded into the vulnerability model. The paper presents the mathematical and conceptual development of each approach, example outputs from each for the same scenario, and a discussion of weaknesses and strengths of each approach. 

Eighteen years after Hurricane Charley made landfall in 2004, Hurricane Ian made landfall in nearly the same location, also as a Category 4 hurricane. Unlike Hurricane Charley (2004), water more so than wind was the impetus behind the disaster that unfolded. Despite being a below-design-level wind event, the large windfield drove a powerful storm surge as much as 13 ft high (relative to the NAVD8 vertical datum) in the barrier islands of Sanibel, Ft. Myers Beach, and Bonita Beach. Flooding was extensive along not only the Florida coast, but also well inland into low-lying areas as far north as Duval County and the storm’s second landfall site in South Carolina. As such, Hurricane Ian will likely be one of the costliest landfalling hurricanes of all time in the US, claiming over 100 lives. The impacts from Hurricane Ian were most severe in the barrier islands from the combination of storm surge and high winds, with many buildings completely washed away, and others left to deal with significant scour and eroded foundations. Several mobile/manufactured home parks on the barrier islands fared particularly poorly, offering little to no protection to anyone unfortunate enough to shelter in them. The damage was not restricted to buildings, as the causeways out to the barrier islands were washed away in multiple locations. In contrast, wind damage from Hurricane Ian appears less severe overall relative to other Category 4 storms, perhaps due to a combination of actual wind intensity being less than Category 4 at the surface at landfall, and the improvements in building construction that have occurred since Hurricane Charley struck 18 years earlier. It is notable that extensive losses were in part driven by decades-long construction boom of residential structures in Ft. Myers and Cape Coral since the 1950s and 1960s, expanding communities and neighborhoods encroaching upon vulnerable coastlines. Beyond serving as an important event to validate current and evolving standards for coastal construction, Hurricane Ian provides a clarion call to reconsider the ramifications of Florida's coastal development under changing climate. This project encompasses the products of StEER's response to this event: Preliminary Virtual Reconnaissance Report (PVRR), Early Access Reconnaissance Report (EARR) and Curated Dataset.