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Free, publicly-accessible full text available November 1, 2025
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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.more » « less
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A 1:16 scaled physical model was constructed to investigate the effectiveness of a seawall, a submerged breakwater, and mangrove forests to mitigate overland flooding and forces on structures in an idealized urban coastal environment. The experiment was performed using tsunami-like waves at different water levels, wave amplitudes, and time scales to simulate long-wave dynamics. The baseline condition (no mitigation), seawall, submerged breakwater, and mangrove forest were tested individually, and the seawall and submerged breakwater were also tested in combination. Wave gauges, acoustic Doppler velocimeters, loadcells, and pressure gauges were used to measure wave elevations, velocities, forces, and pressures on coastal structures, respectively. The performance of these hard structures and mangroves was compared through their effects on wave elevation, particle velocity, and force reduction. Experimental results showed that each protecting structure reduced the horizontal wave forces and inland flow hydrodynamics in the low-water-level case, with a similar performance by the individual seawall, submerged breakwater, and four rows of mangroves. The combined configuration, when the seawall and submerged breakwater were installed simultaneously, caused the most significant maximum force percent reduction by approximately 50%, while mangrove forests arranged in eight rows resulted in a force reduction of 46% in the first building array. However, in the high-water-level cases, the impulsive force measured with the presence of the submerged breakwater was larger than in the baseline case; thus, the submerged breakwater may amplify the impulsive force on the vertical building rows for certain incident wave conditions. Generally, the combined hard structures induced the lowest force reduction factor measured in almost every building row compared to the seawall, submerged breakwater, and mangroves considered separately for all wave conditions and water levels. That means this multi-tiered configuration showed better performance than individual alternatives in reducing horizontal forces inland than the individual alternatives considered separately.more » « less
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Abstract Establishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from spike protein sequences of alpha and beta coronaviruses and their binding annotation to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among coronaviruses. Three viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 and Pipistrellus abramus bat coronavirus HKU5-related (both MERS related viruses), and
Rhinolophus affinis coronavirus isolate LYRa3 (a SARS related virus). We further analyze the binding properties of BtCoV/133/2005 and LYRa3 using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-CoV-2 and all viral sequences released after the SARS-CoV-2 was published. The results predict the binding of SARS-CoV-2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events. -
The 2022 Natural Hazards Research Summit drew researchers, practitioners, and federal agency representatives together to reflect on the accomplishments achieved by the Natural Hazards Engineering Research Infrastructure (NHERI) community and to chart the path for the next decade of impactful natural hazards research. Convened in October, 2022 in Washington, D.C. with support from the National Science Foundation, the specific goals of the two-day Summit were to: (i) discuss and elucidate the research needs for the next 10 years, (ii) foster connections between the broader natural hazards community, and (iii) disseminate information on the resources and capabilities that NHERI offers to researchers focused on preventing natural hazards from becoming societal disasters. This report documents the findings and recommendations from the panel, town hall sessions, and visioning activities that took place at the Summit. The intended audience for the report is the natural hazards research community and the funding agencies that support its research. Accordingly, the report includes a research agenda developed with input from the Summit participants.more » « less
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Coastal and nearshore communities face increasing coastal flood hazards associated with climate change, leading to overland flow and inundation processes in the natural and built environments. As communities seek to build resilience to address these hazards, natural infrastructure (e.g., emergent vegetation) and hybrid designs have been identified for their potential to attenuate storm-driven waves and associated effects in developed nearshore regions. However, challenges remain in robustly characterizing the performance of natural systems under a range of incident hydrodynamic conditions and in bridging interdisciplinary knowledge gaps needed for successful implementation. This paper synthesizes field and laboratory results investigating the capacity of Rhizophora mangle (red mangrove) systems to mitigate wave effects. Results indicate that R. mangle forests of moderate cross-shore width have significant effects on wave transformation and load reduction in sheltered inland areas. Opportunities for future interdisciplinary collaborations are also identified.more » « less