To develop an AI-enhanced 3D reconstruction workflow that integrates UAV imagery with existing aerial and satellite data to generate high-resolution, real-time georeferenced models of coastal and watershed features in the southern Lake Michigan region. To apply this system to monitor and quantify environmental changes—such as shoreline erosion, dune morphology, stormwater runoff, and infrastructure vulnerability—before, during, and after extreme weather events or seasonal transitions. To evaluate the performance and accuracy of state-of-the-art reconstruction methods (VGGT, MASt3R, DUSt3R) for coastal applications, using ground-truth data (e.g., GNSS, LiDAR) to validate outputs and assess model limitations under varying conditions. To create an open-access toolkit and decision-support platform—including a web-based dashboard and immersive VR/MR interface—that enables stakeholders to visualize 3D results and extract actionable metrics (e.g., erosion rates, flood extent, asset risk). To engage with community stakeholders and IISG outreach specialists from project inception to ensure research findings are translated into practice through training workshops, user guides, and integration with local planning and public outreach efforts.

The ultimate goal of this proposed study is to develop a near-real-time system for flood inundation mapping and flood depth estimation, finally enabling timely assessments of community transportation network disruptions during flood events. As part of this effort, three key anticipated outcomes will be delivered: (1) An off-the-shelf flood inundation mapping model; (2) an algorithm that estimates flood depth by integrating flood inundation maps with digital elevation models, and (3) an algorithm that assesses transportation system disruptions by combining flood information with real-world road network data. 

This one-year research project is dedicated to investigating the viability of utilizing dredged sludge from the Great Lakes as a substitute for fine aggregates in cementitious composites. The research encompasses experimental investigation such as the chemical and physical properties of the dredged sludge and the fresh and mechanical performance of cementitious composites incorporating this material. Subsequently, a “cradle to gate” life cycle assessment will be conducted for cementitious composites with dredged sludge.

The primary goal of this project is to propose an innovative recycling approach for managing pollutant-laden dredged sludge, aiming to reduce the reliance on traditional sand in the construction industry.

Submerged, “reef-style” breakwaters may be a viable nature-based solution for shoreline protection and ecological enhancement. However, the lack of quantitative data on the effectiveness of such nature-based solutions limits the ability of managers to implement them within Great Lakes coastal communities.

Our goals are to use monitoring data to (a) inform habitat restoration and protection efforts around Lake Michigan and other Great Lakes, and (b) provide the much needed geomorphic and sediment-routing context to help assess the regional impacts of such structures (and their broader utility). We will accomplish these goals through the following objectives: Obj 1) Determine the effect of the two different artificial reefs on aquatic species abundance and diversity through comparisons to long-term (2016-present) ecological monitoring sites co-located with the reefs. Obj 2) Quantify bathymetric changes to the nearshore environment surrounding the reefs and topographic changes to the adjacent beach environment, evaluating post-reef morphodynamics in context of available pre-construction data (post-2018 at Site 1). Obj 3) Engage a variety of coastal stakeholder groups by way of discussion forums, workshops, fact sheets, or other meeting types. 

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.