Nina Stark

Associate Professor University of Florida

Gainesville FL

Nina Stark's expertise is in geotechnical storm and flood reconnaissance and mitigation.

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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

Mitigation

Geotechnical Storm

Structure-Seabed Interactions

Sediment Dynamics

Coastal and Marine Geotechnics

Seabed Geomechanics

Coastal and Riverine Morphodynamics

Beach Trafficability

Flood Reconnaissance

Social

Articles

Geotechnical Properties from Portable Free Fall Penetrometer Measurements in Coastal Environments

Journal of Geotechnical and Geoenvironmental Engineering

Reem 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 Engineering

Julie 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 Access

Mingyu 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.