Rapid soil classification and integration of soil characteristics for UXO site characterization and risk assessment

The overarching goal of project is to investigate the interactions between geotechnical seabed soil properties and behavior, physical sediment dynamics, and benthic biogenic processes towards the rapid geotechnical site characterization of seabed sediments using portable free fall penetrometer for enhancement of acoustic seabed site classification, assessment of UXO mobility and remediation needs, and generally UXO risk assessment. The research objectives are to: i) develop a soil behavior classification scheme based on portable free fall penetrometer measurements; ii)  identify effects of benthic biogenic processes on geotechnical soil properties and integrate these effects in the soil classification scheme; iii) identify and quantify the impacts of the different soil classes on acoustic UXO detection and classification methods, on erodibility estimates, and on susceptibility to soil liquefaction processes; and iv) develop strategies to implement the effects of soil classes into UXO risk assessment.

Sponsor: Strategic Environmental Research and Development Program (SERDP, Home (serdp-estcp.org)), grant MR21-C1-1265

Team members:

• Nina Stark (PI, University of Florida)
• Adrian Rodriguez-Marek (PI) and Md Rejwanur Rahman (PhD student) (both Virginia Tech)
• Grace Massey (PI) and Carl Friedrichs (PI) (both Virginia Institute of Marine Sciences)
• Kelly Dorgan (PI) and Chesna Cox (MS student) (both Dauphin Island Sea Lab)

Assessment of trafficability of coastal sediments from satellit based remote sensing

Insufficient trafficability of coastal soils represents a major uncertainty and risk to naval missions, as well as for rescue and evacuation missions in coastal environments. In most cases, on-site physical testing and in-situ determination of geotechnical properties that enable an assessment of trafficability is unfeasible due to access or timing restrictions. Thus, the long-term goal of this work is to develop relationships between geotechnical properties of coastal sediments, satellite-based remotely sensed data, and coastal processes to predict trafficability for a variety of vehicles with high confidence.

Towards this long-term goal, the objectives of the proposed study are:

1. Detect and map coastal sediment dynamics in the intertidal zone from satellite images and relate them to geotechnical properties – and the variability thereof – relevant for the assessment of trafficability, including fines content, water content, relative density, and bearing capacity.
2. Develop probability thresholds predicting the trafficability of a person, a wheeled vehicle, and a hovercraft for a wide range of typical coastal sediments and geotechnical characteristics. Apply probability thresholds to trafficability assessment based on geotechnical properties derived from satellite imagery and assess uncertainty.

Sponsor: Office of Naval Research (ONR, www.onr.navy.mil), N00014-23-1-2418

Team Members:

• Nina Stark (PI) and Stephen Adusei (PhD student) (both University of Florida)
• Fred Falcone (PhD student, Virginia Tech)
• Julie Paprocki (PI, University of New Hampshire)

Integration of soil mechanics in numerical models of surf zone beach processes via joint field observation and numerical modelling

The longterm goal is to predict rapid beach evolution and the associated variations in seabed soil strength and textural seabed properties due to storms by integrating soil mechanics into regional-scale morphodynamic models with the aim of assisting with model calibrations for acoustic surveying and navigation, as well as with trafficability assessment from remote sensing.

To achieve the longterm goal, the research aims at understanding the relationship between local geomorphodynamics and geotechnical properties of the seabed and intertidal zone sediment surface layers by collecting, analyzing, and correlating field data, and improving numerical modeling tools with the following objectives:

1. Analyzing and preparing existing field data of geotechnical properties for numerical model validation.
2. Performing new field experiments supported by laboratory soil characterization specifically designed to advance fine-scale and regional scale models’ soil mechanics capability.
3. Evaluate the sensitivity of the short-wave-averaged regional-scale morphodynamic model XBeach for various parameterizations relevant to sediment transport and erodibility.
4. Improving and validating the free-surface resolving Eulerian two-phase modeling framework, SedFoam, for simulating sediment transport under waves with proper geotechnical seabed properties to improve erodibility related sub-models in XBeach.
5. Assess and evaluate the relationship between geotechnical properties and local geomorphodynamics in the surf zone during storm events via a synthesis of observational data, fine-scale and regional-scale model data.

Sponsor: Office of Naval Research (ONR, www.onr.navy.mil), N00014-22-1-2398

Team Members:

• Nina Stark (PI) and Saurav Shrestha (PhD student) (both University of Florida)
• Tom Hsu (PI) and Jiaye Zhang (PhD student) (both University of Delaware)
• Adrian Rodriguez-Marek (PI, Virginia Tech)
• Patrick Dickhudt (U.S. Army Corps of Engineers)
• Jonathan Hubler (Villanova University)

Planning under uncertainty for advanced networks of sensors and effectors

Algorithms for planning and operating the network under a wide variety of sources of uncertainty (Stilwell), e.g., placement uncertainty, movement, failure, sensor performance (including due to environmental effects), uncertainty in effector node performance, non-stationary target trajectory densities, etc.

Probabilistic framework that describes sensor/effector node mobility and performance with respect node properties and environmental conditions (Stark), e.g., node characteristic (geometry, weight, sensor types) and environmental conditions (seabed properties, geomorphodynamics, local hydrodynamics)

Sponsor: Office of Naval Research (ONR, www.onr.navy.mil), N00014-20-1-2845

Team Members:

• Nina Stark (PI) and Saurav Shrestha (PhD student) (both University of Florida)
• Dan Stilwell (PI) and Mingyu Kim (PhD student) (both Virginia Tech)

Seafloor characterization from free fall penetrometers using machine learning

Rapid seafloor surface sediment characterization is important for many naval applications including navigation in areas of active sediment dynamics, mine burial prediction, sensor placements, unexploded ordnance detection and classification, to name just a few. Free fall penetrometers (FFP) offer a means to rapid seafloor sediment characterization from any vessel of opportunity and in a wide range of environmental conditions. FFP seabed profiling has found to be reliable and accurate, and methods are available to derive seabed stratification including layer thicknesses, to classify sediment type, and to estimate geotechnical properties such as undrained shear strength, friction angles, and relative density. However, those data analysis methods can be complex and currently require expert users. The proposed work focuses on using a large existing FFP deployment and sediment information database to develop a machine learning model to facilitate FFP data analysis with high accuracy but without need for expert users. The research tasks include: the preparation of the database, expansion of a current numerical model simulating FFP deployments for sensitivity analysis and investigation of physical processes leading to the seabed specific FFP profiles, development of a machine learning model for FFP data analysis, and finally, assessment of the accuracy of FFP results in comparison with seabed coring. Integration of students into naval research is a core objective of this proposal. At least two graduate students and two undergraduate students will be actively involved in the research. The team includes leading experts in FFP development and data analysis, numerical simulations and probabilistic analysis in geotechnical engineering, and physics-informed machine learning.

Sponsor: Naval Engineering Education Consortium (NEEC)

Team Members:

• Nina Stark (PI) (University of Florida)
• Alba Yerro Colom (PI), Adrian Rodriguez-Marek (PI), Anuj Karpatne (PI), Elsbeth Noe (undergrad researcher), Elise Hummel (MS student), and Jonathan Moore (PhD student) (all Virginia Tech)