Predicting Natural Anchoring from Roots to Landscapes Through Laboratory Experiments and Reduced-order Modeling

Abstract

Plant roots act as natural soil anchors in a wide variety of landscapes, stabilizing riverbanks and coasts against erosion, reducing the likelihood of landslides, and slowing soil creep. Plants are therefore used in land management to prevent erosion and mitigate natural hazards on engineered and natural slopes. Despite the pervasive effects of root anchoring on landscapes, there is not yet a comprehensive theory for describing anchoring mechanisms and predicting anchoring forces. The proposed research will develop a new framework for predicting anchoring forces in cohesive, granular soils through a combination of laboratory experiments and model development. The project will consist of five tasks: Task 1: Laboratory experiments to measure the forces on simple objects moving through cohesive, granular soil. Task 2: Laboratory experiments to measure the forces required to uproot synthetic root systems. Task 3: Development of a continuum model for cohesive soil that can match the results of the laboratory experiments. Task 4: Development of a new cohesive resistive force theory (RFT) that can match the results of the laboratory experiments while offering enhanced simplicity and efficiency. Task 5: Development of a framework for predicting uprooting forces in arrays of multiple plants, drawing on continuum models and RFT. The outcome will be a dramatically improved understanding of the fundamental physics of natural anchoring, which will lay the groundwork for more robust models of the strength of vegetated landscapes. This advance requires a collaboration between geoscientists who study soil and erosion under widely varying natural and experimental conditions, and mechanicians who develop theoretical and computational models of granular physics. The new understanding of anchoring will influence fields as diverse as engineering, ecology, and hazard management. The new modeling tools and experimental data will extend to other scenarios that involve objects interacting with soils, including biological or robotic locomotion, tunneling by animals or machines, off-road vehicle design, and dynamic penetration or impacts. The project will address the ARL competencies Mechanical Sciences and Sciences of Extreme Materials. This abstract is available for public release during the evaluation phase and should an award be made afterward.

Document Details

Document Type
DoD Grant Award
Publication Date
May 24, 2023
Source ID
W911NF2310227

Entities

People

  • J. Taylor Perron

Organizations

  • Army Contracting Command
  • Massachusetts Institute of Technology
  • United States Army

Tags

Readers

  • Agricultural Chemistry/Soil Science
  • Distributed Systems and Data Platform Development
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - Autonomous System Control