High lake levels have reduced beach sizes across Chicago, but we have little understanding of how much was passive inundation versus sediment remobilization. Ongoing collaborative efforts with the Chicago Park District and the Illinois Coastal Management Program are focused on observations of process-landform dynamics using camera arrays at select beaches and integrating UAS-based imagery, topographic information, wave data, and camera footage. However, while efforts are underway to understand the subaerial dynamics here (e.g., shoreline behaviors), little is known about littoral dynamics and sand transport across the highly fragmented urban nearshore environment, where prior studies have inferred a complex lakefloor geology that includes outcropping Silurian bedrock reefs, heavily scoured and dissected glacial clay tills, and thin, discontinuous sand veneers. We wish to capture the geologic configuration of the nearshore at the surface and map the shallow subsurface architecture as a means of quantifying sand volumes and relating them to the broader geologic template and the urban infrastructure with its influence on nearshore hydrodynamics.

• Develop a historical database of environmental processes and anthropogenic disturbances determined to have caused coastal geomorphic changes for Illinois Beach State Park and Waukegan Beach
• Produce list of probable future (within next 50 years) beach and dune changes for coastal managers
• Engage stakeholders in order to understand how data can be readily visualized and utilized in the planning and decision-making process

• Build a network (“PhragNet”) to serve as the foundation for an adaptive management framework focusing on reducing uncertainty associated with Phragmites management

• To provide information on past low lake levels of Lake Michigan, to better
  understand why there have been lower lake levels in the past, and to better predict and thus prepare for future low lake levels

This research project proposes a computational model validated by experimental testing to improve various characteristics of sustainable cement and concrete by reusing waste materials from the Great Lakes as additives in sustainable building materials. The model will be paired with AI algorithms to efficiently determine the feasibility of recycling bottom sediment from the Great Lakes as a sustainable construction material and analyze the impact of the additive on concrete performance. The goal of the proposed research is to reduce environmental pollution and improve the current ecological system by recycling the bottom sediments in the Great Lakes region, thus improving the efficiency of concrete use in actual construction and the ecological sustainability of the Great Lakes region.

• Define historical baselines for restoration of coastal Great Lakes pine forests
• Evaluate the necessity for restoration of composition and structure in modern coastal forests
• Assess the impact of historical legacies on modern composition and structure of coastal pine forest