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This experimental project investigated the debris accumulation in front of structures during tsunamis (debris damming), which leads to an increase in the forces imposed by tsunami flow on structures. The study was conducted through the construction of a 1:20 geometric scale physical model. Tsunami-like waves were generated over an idealized slope and transported different shapes of multi-debris, representing shipping containers, over the flat test section to measure debris loadings on elevated column structures. The experiment optically measured the debris impact and damming process, along with the corresponding loads on the entire column structure using a Force Balance Plate and separately on an individual column in the front row using a load cell. This unique data set will help to understand the impact of various factors on debris-driven damming loads, including wave characteristics, specimen configurations, and debris shapes. This data will also help to develop and validate numerical models that predict the motion and dynamics of floating debris during extreme coastal events. This project is the outcome of “Collaborative Research: Experimental Quantification of Tsunami-driven Debris Damming on Structures” with collaborators from the University of Hawaii at Manoa, Louisiana State University, and Oregon State University. 

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. 

There has been a growing interest in research on how to define and build indicators of resilience to address challenges associated with sea-level rise. Most of the proposed methods rely on lagging indicators constructed based on the historical performance of an infrastructure sub-system. These indicators are traditionally utilized to build curves that describe the past response of the sub-system to stressors; these curves are then used to predict the future resilience of the sub-system to hypothesized events. However, there is now a growing concern that this approach cannot provide the best insights for adaptive decision-making across the broader context of multiple sub-systems and stakeholders. As an alternative, leading indicators that are built on the structural characteristics that embody system resilience have been gaining in popularity. This structure-based approach can reveal problems and gaps in resilience planning and shed light on the effectiveness of potential adaptation activities. Here, we survey the relevant literature for these leading indicators within the context of sea-level rise and then synthesize the gained insights into a broader examination of the current research challenges. We propose research directions on leveraging leading indicators as effective instruments for incorporating resilience into integrated decision-making on the adaptation of infrastructure systems. 

The response leveraged small, self-contained, regional FASTs deploying in phases to collect rapid assessment data using vehicle-mounted street-level panoramic imaging platforms, with select use of UAS. Routes were selected to ensure longitudinal data capture of areas previously documented for Hurricane Laura, as well as new clusters exposed to some of the Delta’s highest wind speeds to the east of landfall. 

This study describes a hybrid framework for post-hazard building performance assessments. The framework relies upon rapid imaging data collected by regional scout teams being integrated into broader data platforms that are parsed by virtual teams of hazards engineers to efficiently create robust performance assessment datasets. The study also pilots a machine-in-the-loop approach whereby deep learning and computer vision-based models are used to automatically define common building attributes, enabling hazard engineers to focus more of their efforts on precise damage quantification and other more nuanced elements of performance assessments. The framework shows promise, but to achieve optimal accuracy of the automated methods requires regional tuning.