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.

During the given one-year research period, I plan to develop a data-driven model integrating the physical model of infrastructure vulnerable to hazard and artificial intelligence machine learning algorithms to offer precautions and suggestions to resist natural hazards and enhance infrastructure flood resilience for the southern Lake Michigan communities. The proposed research targets to provide coastal communities with on-time and accessible suggestions to resist flooding attacks, support coastal industrial development without interference, give organizations reasonable, efficient recommendations to minimize the flooding impact on infrastructure, and offer the government customized design advice for infrastructure in the southern Lake Michigan region. Most importantly, this research will call public attention to the resilience of coastal communities and infrastructure.

Quantifying the introductory impacts of an artificial reef on the nearshore community in Lake Michigan have not been made a priority in any experimental design. This project aims to mend this lack of understanding by combining biology and geology to create a new understanding of the effects of a newly constructed artificial reef (Rubble Ridges). This will be accomplished by observing the effects of sediment accumulation around a newly constructed artificial reef on benthic community diversity and growth. Sampling at the reef site will include sampling for invertebrates, encrusting organisms, and fish once a month during the sampling season. We will compare the trends from the Rubble Ridges site to the trends from the control site.

The objectives for this study are (1) to determine the difference in abundance and diversity of benthic organisms between the submerged shoreline stabilization structure site and the control site over a 2-year period, (2) to determine if the development of nearshore artificial reefs positively impact benthic communities in Southern Lake Michigan, and (3) to determine if benthic community diversity and abundance changes due to sediment accumulation and changes in sand grain size. We hypothesize that there will be an increase in abundance and diversity in benthic communities at the submerged shoreline stabilization structure site in comparison to the control site, that these artificial reefs will have a positive impact on the benthic communities in Southern Lake Michigan, and that sediment accumulation and larger sand grain size provide suitable habitat areas that will increase the diversity and abundance of the surrounding benthic community. Project managers and scientists can utilize this information in upcoming projects, whose intent is to prevent shoreline erosion, as a means to further understand this relationship.

This project proposes a computational framework to efficiently simulate the flood-infrastructure interaction mechanism, assess the impact and risk of flood on the infrastructure in the southern Lake Michigan region and provide recommendations on the selection of rational infrastructure types suitable for the flooding area. The goal of the proposed research is to mitigate potential losses, improve the current post disaster reconstruction strategy and therefore enhance the flood resilience of the infrastructure and coastal communities in the Great Lake region. Key outcomes include an extensive literature review on the flood hazard data and infrastructure damage data in the southern Lake Michigan region, and a computational framework that integrates the fluid-structure interaction model and flood risk assessment model.

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.

Wetlands provide ecosystem services critical to the well-being of human populations, yet they have undergone massive loss and degradation. Illinois and Indiana alone have lost 85-90% of their historical wetland extent, which could impact the region’s resilience to climatic events and stressors. In response, agencies are dedicating substantial resources to restoring wetlands and their ecological functions. However, maintaining high quality, resilient habitats in human-dominated landscapes is challenging. Current literature reports a wide range of response to restoration interventions. Gathering long-term, consistent data on restored and protected wetlands is key to advancing our understanding of the root causes of this variability. This project will identify remote sensing-based indicators of vegetation composition and ecological functions to facilitate the consistent and large-scale monitoring of Lake Michigan wetlands. As a result, the project will generate three outputs aligned with the strategic goals of the IISG: (1) a literature review, to be published in a peer-review journal, summarizing current knowledge on the relationships between remote sensing-based indicators and transformations in plant communities; (2) a detailed script and tutorial, to be made available to scholars and stakeholders, showing users how to derive indicators of wetland health and recovery from free remote sensing datasets; and (3) a case study in a subsample of wetlands to serve as a proof of concept for the larger proposal.