ENVE 

Supervising Faculty: Dr. Guiling Wang 

Project Title: Recent Changes in Winter Precipitation in Connecticut and Their Impact on Streamflow Dynamics 

URA duties and responsibilities: The URA will conduct analysis of winter precipitation or streamflow variability with temperature based on past station data in Connecticut, in collaboration with a PhD student. The streamflow of interest includes winter high flow events and summer low flows; the precipitation variables include rainfall, snowfall, and snowmelt. The URA will choose which variable(s) to work on, and will be responsible for (1) downloading the data from the web, (2) analyzing how the variable of interest has changed over time and how temperature may have played a role in the temporal dynamics of precipitation or streamflow, (3) presenting research progress and findings in the form of PPT and a summary report at the end. 

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Supervising Faculty: Maria Chrysochoou 

Project Title: A thermodynamic approach to chemical soil stabilization

URA duties and responsibilities: The URA will work with Ph.D. student Tasneem Ahmadullah to test stabilized clay samples that have cured for up to one year. Lab duties will involve testing of strength, pore water extraction, and assistance with follow-up tests on the solid and extracted pore solution. 

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Supervising Faculty: Maria Chrysochoou 

Project Title: Pyrrhotite Oxidation in Crumbling Concrete Foundations 

URA duties and responsibilities: The URA will work with M.S. student Naomi Adler to assist with laboratory experiments of pyrrhotite (iron sulfide) and aggregate oxidation under controlled conditions, to inform the development of models for the reaction and deterioration of crumbling foundations. 

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Supervising Faculty: Marina Astitha 

Project Title: Extreme weather forecasting for the Northeast US. 

Description of work: “Analyze in-house storm database (>200 simulated storms) by storm type: rain/wind, thunderstorms, blizzards/Nor’easters, tropical storms. The analysis will be based on weather variables that are important for prediction of power outages. Assist a graduate student in running new simulations to improve storm predictions.” Students will be educated in the research of forecasting extreme storms in the NE US using various weather models to improve storm predictions.  

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Supervising Faculty: Marina Astitha 

Project Title: Assessment of lake eutrophication using machine learning and multi-media modeling systems. 

Description of work: “Analyze data on atmospheric nitrogen deposition, lake dissolved oxygen and fertilizer applications for Lakes Erie and Michigan. Data will be acquired from observations and model simulations. Assist the graduate student in developing the machine learning model and analyze preliminary results.” Students will become familiar with multi-media modeling systems encompassing air, water and soil conditions, as well as machine learning models developed to predict lake water quality. 

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STAM 

Supervising Faculty: Wei Zhang 

Project Title:  Data Processing and Learning for Coastal Resilience 

URA duties and responsibilities: Many coastal communities suffered from multiple natural hazards, such as hurricanes and winter storms. Strong winds, flooding, high surges, and waves imposed significant damages on coastal infrastructure.  Many coastal infrastructures, after years of service in varied operational environments and scenarios, might have experienced accumulated damages in different critical structural locations. As many catastrophic disasters originate from local damages/failures, effective decision making, especially when damages/cracks are developed in the early age of life-cycle, are extremely important. Without quantifying the risk level related to structural safety, it has been a dilemma of safety concern and the huge cost of replacing these civil infrastructures, from bridges, buildings, to offshore platforms, for the decision-makers and owners. Recently, with more available data on natural environments, such as wind speed and wave data from Ocean Observatories Initiative (OOI) funded by National Science Foundation (NSF), NOAA buoy databases, and the NOAA tides and current database, etc., it is possible to perform data processing on to observe the correlations between different environmental parameters. Combined with the state-of-the-art machine learning methods and integrated physics-based modeling of structure-environment interactions developed in our group, it is promising to perform a physics-informed deep learning model for various types of coastal infrastructure. Students will work with our graduate students in data processing and learning models for different types of civil infrastructures.   

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Supervising Faculty:  Ramesh B. Malla 

Project Title: Design and Analysis of Lunar and Deep Space Habitat Structures 

Description of work: The research team supervised by Prof. Ramesh B. Malla intends to host two students with competencies in physics, math, and structural and mechanics-related engineering fundamental knowledge. Students at the level of Junior or Senior are preferred. The students should have relevant coursework on mechanics, structural analysis, design, modeling, and construction materials. These competencies are necessary to contribute to the research of development and analysis of Space structures/habitats, and construction of a physical testbed, meant to simulate a Lunar habitat and associated environmental hazards. This research requires working with computer models to simulate conditions experienced on the Lunar surface (including temperature fluctuations, meteorite impact, and radiation), construction of a model Lunar habitat for physical testing, and working closely with other researchers in the lab to deliver models and reports. Students with MATLAB and/or other computer programming and experience with structural design and analysis software are preferred.  Successful applicants stand to gain exposure to professionals in industry and government agencies, get the opportunity to work on NASA-funded projects researching deep space structures/habitats, improve their knowledge of structural analysis and design, and gain experience working in a research environment.  

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Supervising Faculty:  Ramesh B. Malla 

Project Title: Analysis, Modeling, Testing, and Monitoring of Railroad and Highway Bridge Infrastructure 

Description of work: The research team supervised by Prof. Ramesh B. Malla intends to host two students with a good understanding of physics, math, structural, and mechanics-related engineering fundamental knowledge.  The student would have the opportunity to work on a project funded by the U.S. Department of Transportation (DOT) University Transportation Center (UTC) - Transportation Infrastructure Durability Center (TIDC).   The research team’s current effort includes finite element modeling, analysis, and testing of railroad bridges to study dynamic behavior and to develop suitable structural health/condition monitoring systems for the bridge structures. Students at the level of Junior or Senior are preferred. The students should have relevant coursework on mechanics, structural analysis, design, modeling, and experience with structural design and analysis software engineering computer software. Successful applicants will gain exposure to industry and government agency professionals and get the opportunity to utilize their classroom knowledge to solve real-life problems, improve their knowledge on structural dynamics, analysis, and design, and gain experience working in a research environment.  

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TUE 

Supervising Faculty: Prof. John Ivan 

Project Title: Safety Assessment of New England Roadways during the COVID-19 Pandemic 

URA duties and responsibilities: The selected undergraduate student will work with Prof. Ivan to acquire and prepare Connecticut road traffic data sourced from smart technologies through several different platforms available through Connecticut Department of Transportation (CTDOT). The student will also gather data from permanent count stations on Connecticut Highways (also from CTDOT) which covers a limited number of locations on the road network, and also gather highway crash data through the Connecticut Crash Data Repository (ctrash.uconn.edu). The student will process all of these data into a form suitable for estimating models of speed and crash incidence and severity.