Nina Stark
Associate Professor | University of Florida
Gainesville, FL, UNITED STATES
Nina Stark's expertise is in geotechnical storm and flood reconnaissance and mitigation.
Biography
Nina Stark is the faculty lead of the coastal and marine geotechnics research group with expertise in geotechnical storm and flood reconnaissance and mitigation. She has overseen coastal and riverine extreme event reconnaissance, impact mitigation, and community resilience e.g., during Hurricane Harvey (2017), Hurricane Irma (2017), Tropical Storm Melissa (2019), western European floods (2021), Yellowstone Flood (2022), and Hurricane Idalia (2023).
Areas of Expertise (9)
Mitigation
Geotechnical Storm
Structure-Seabed Interactions
Sediment Dynamics
Coastal and Marine Geotechnics
Seabed Geomechanics
Coastal and Riverine Morphodynamics
Beach Trafficability
Flood Reconnaissance
Articles (3)
Geotechnical Properties from Portable Free Fall Penetrometer Measurements in Coastal Environments
Journal of Geotechnical and Geoenvironmental EngineeringReem Jaber & Nina Stark
2023-12-01
Coastal environments are characterized by a variety of sediment deposits with highly diverse geotechnical properties. Particularly in energetic coastal environments, sediment type and properties may vary on small spatiotemporal scales, limited previous information may be available, and sediment coring may be difficult.
Geotechnical Characterization of a Tidal Estuary Mudflat Using Portable Free-Fall Penetrometers
Journal of Geotechnical and Geoenvironmental EngineeringJulie Paprocki, et. al
2023-11-08
This study investigates the geotechnical characteristics of a soft tidal mudflat in the Great Bay Estuary, New Hampshire. Laboratory testing of surficial sediment samples of the upper 10 cm and field observations from a portable free-fall penetrometer (PFFP) were used to characterize soil strength properties (coefficient of consolidation and undrained shear strength).
Toward optimal placement of spatial sensors
IEEE AccessMingyu Kim, et. al
2023-07-01
This paper addresses the challenges of optimally placing a finite number of sensors to detect Poisson-distributed targets in a bounded domain. We seek to rigorously account for uncertainty in the target arrival model throughout the problem. Sensor locations are selected to maximize the probability that no targets are missed.
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