Despite their significance, rural areas have historically been underrepresented in research and disproportionately underserved in terms of infrastructure and community development. Rural areas, characterized by low population density, agricultural-based economies, and localized transportation networks, present unique challenges and opportunities for civil and environmental engineering. However, the increasing challenges posed by climate change, including extreme weather events and shifting environmental dynamics, underscore the pressing need to prioritize sustainability and resilience initiatives within rural areas to ensure the long-term prosperity and well-being of these vital communities.
In this ten-week summer research program, students will work with faculty in the Department of Civil and Environmental Engineering to conduct research and will contribute new knowledge to improve our understanding of how best to address the challenges facing rural environments. Through collaboration with industry partners, students will also be given opportunities to learn how infrastructure challenges are currently being addressed by the civil and environmental engineering industry. In addition, this program offers a series of communication development opportunities including preparation of a conference paper, informal presentations to their peers, formal poster presentations, and outreach to high school students.
Competitive stipend: $7,000
Suite-style room and meal plan
Travel expenses to and from Lincoln
Campus parking and/or bus pass
Full access to the Campus Recreation Center and campus library system
Advanced nanomaterials and manufacturing for PFAS remediation
Significance: Per- and polyfluoroalkyl substances (PFAS) is an emerging pollutant that has become a major threat to the environment and public health. PFAS compounds are difficult to degrade in the natural environment as well as conventional water treatment processes and can accumulate within human body leading to different diseases. Our group designs sustainable nanomaterials and nanotechnology with unique capabilities to treat and remove PFAS compounds from our drinking water, ground water or wastewater. We are also combining 3D printing approaches to design nanotechnology-based water filters that can simultaneously separate and degrade PFAS from water. The objective of this research is to design catalytic nano-filters using 3D printing. We will design novel filters, characterize their mechanical, physical, and chemical properties, and also, we will identify their capabilities to achieve PFAS adsorption and/or degradation.
Student Participation: The REU student will synthesize new nanocomposite-based 3D printing ink, design the new filter via 3D printing the previously synthesized ink, perform PFAS adsorption experiments, and analyze the obtained data to conclude about the performance of the new filter for PFAS adsorption/degradation. Student Outcomes: The REU student will learn about the impact of nanotechnology and 3D printing in designing novel water filter. The student will also learn about PFAS pollution problem and will be able to critically think about solutions to this problem. Prerequisite Knowledge and Training: No formal course prerequisites.
Significance: The occurrence of microplastics, an emerging contaminant in agricultural systems, is very poorly characterized. Plastics are a frequently observed component of marine debris and there is growing concern about microplastic ecotoxicity, and the impacts of sorbed hazardous organic contaminants, heavy metals and biofilms on microplastic surfaces. However, microplastics are increasingly being found in terrestrial freshwater environments in addition to marine systems. To date, there is little information about how surrounding land use affects the concentrations of microplastics in freshwater streams. The primary research question to be addressed in this project is how concentrations of microplastics in freshwater streams differ between agricultural and suburban land uses.
Student Participation: The REU student will install nets to gather microplastics from streams in agricultural and suburban areas in Nebraska. The student will separate and characterize the microplastics using stereoscopy. Student Outcomes: The REU student will gain exposure to field research and laboratory training in microplastic characterization methods including density separation and oxidation. The student will learn to use visible light and UV microscopes. Prerequisite Knowledge and Training: No formal course prerequisites. Microscopy training to be conducted during the first week of the program by the graduate student and faculty mentor.
Depopulation and Adaptive Infrastructure Resilience
Significance: A study by the University of Illinois Chicago finds that by the year 2100 close to half of the nearly 30,000 U.S. cities (places with >2500 persons) will face population decline in the range of 12-23%. This major decline in population will bring unprecedented mobility and infrastructure challenges, possibly leading to disruptions in basic services like transit, clean water, electricity, and internet access. Simultaneously, increasing population trends in resource-intensive suburban and periurban cities will likely take away access to much needed resources in depopulating areas, further exacerbating their challenges. This project has two broad research questions: 1. To understand the infrastructure challenges faced by depopulating cities in Nebraska. 2. To define a basic framework for adaptive resilience.
Student Participation: The student will use spatial and statistical analyses using Shared Socioeconomic Pathways (SSPs) that range from environmentally friendly development (green road) to fossil-fueled development (highway). Student Outcomes: The student will produce an academic journal article focused on adaptive infrastructure resilience, with an empirical case study in Nebraska. Prerequisite Knowledge and Training: Prior knowledge includes an ability to think across traditional disciplinary boundaries, an interest in reading widely, and basic data processing skills (in Excel and other similar software). More advanced skills in GIS, data processing, and statistical analysis will be provided by the research mentor and graduate students based on the direction taken in the work.
Multi-Sensor Fusion for Proactive Commercial Motor Vehicle Safety at Work Zone
Significance: The overarching goal is this project is to support commercial motor vehicle (CMV) safety programs with the acquisition of enriched, high-quality data sets of CMV movement in work zone areas and to target unsafe driving of CMVs and non-CMVs with proactive safety-focused countermeasures; that is, real-time guidance to mitigate future crash/near-crash events for approaching vehicles. The project will create co-simulation tools and augment them with a virtual collaborative environment to educate the public, motor carriers, and CMV drivers about these work zone safety enhancement solutions. Many studies have demonstrated that combining data from multiple sensors, such as cameras, LiDAR, and RADAR, has the potential to significantly reduce vehicle detection errors and improve the overall quality of traffic data. However, the effectiveness of fused data and Digital Twin technology for improving work zone safety has not been investigated. To bridge this gap, research is needed to investigate effective ways of multi-sensor data collection to develop work zone safety models, improve the accuracy and reliability of predictive models and ensure that these models can be used to mitigate potential safety issues.
Student Participation: The student will first learn three well-known simulation software, VISSIM (microscopic traffic simulation software), TruckSim (heavy truck dynamics simulation software), and CARLA (autonomous driving simulator). Then the student will develop a co-simulation environment that takes inputs from the three software and output sensor information, vehicle state, and performance metrics related to operational and safety conditions. Lastly, the student will evaluate and design a proactive work zone safety warning system using the co-simulation environment. Student Outcomes: The student will learn about traffic simulation, truck dynamics simulation, and autonomous driving simulation. The student will learn about systems integration. Lastly, the student will learn about the process of designing and conducting simulation experiments, and subsequently, drawing conclusions from the experiments. Prerequisite Knowledge and Training: No formal prerequisite knowledge is required. Training will be provided during the course of the research.
Dr. Xu Li
Civil and Environmental Engineering: Environmental Engineering
Producing Clean Energy and Value-Added Products from Agricultural Wastes for a Circular Economy
Significance: Modernizing the agricultural industry is critical in building a sustainable future. One important component of agricultural modernization is to manage agricultural wastes from the perspective of circular economy. A waste-to-resource approach should be developed to convert the organics in agricultural wastes to clean energy (e.g., hydrogen gas) or value-added products (e.g., medium chain carboxylic acids, or MCCAs). Hydrogen gas is an important form of energy in decarbonizing the economy, while MCCAs can be used as feedstocks to produce valuable chemical compounds. The first objective is to develop and operate a bioreactor system to convert the organics in animal wastes into hydrogen gas or MCCAs. The second objective is to characterize the microbes inside the bioreactor system.
Student Participation: The student will help operate and optimize the bioreactor system to maximize the production of target molecules (e.g., hydrogen gas or MCCAs) from animal wastes. The student will also learn and conduct molecular tests to analyze the microbiome inside the bioreactor system. Student Outcomes: Student will learn both process engineering and molecular techniques through this project. Prerequisite Knowledge and Training: No formal prerequisite knowledge is required. Training will take place on site.
Dr. Yusong Li
Civil and Environmental Engineering: Water Resources Engineering
Micro- and Nanoplastics released from plastic food packaging
Significance: The widespread use of plastic products in food handling poses a direct risk of releasing tiny plastic particles, such as microplastics (less than 5 mm) and nanoplastics (less than 1 µm), into our food. Recent research, including our own, has uncovered alarming findings about these particles being released from plastic food containers, even during typical usage or when usage guidelines are ignored. Some containers can release as many as 4.27 billion microplastics and 2.29 trillion nanoplastics into a liter of water in just three minutes of microwave heating. Our preliminary toxicity study shows that exposure to these particles can result in the death of human embryonic kidney cells. This revelation has prompted pressing public health concerns, with unresolved questions about release circumstances, plastic types, ingestion levels, and toxicity. Moreover, it is vital to explore potential disparities based on demographics and access to alternative containers.
Student Participation: The REU student will help conduct microplastics and nanoplastics release experiments. The student will measure the number of particles released into food. Student Outcomes: The REU student will gain exposure to scientific research, including detecting microplastics and nanoplastics using various instrument. Prerequisite Knowledge and Training: No formal course prerequisites. Students will be trained in the first week of the program by the graduate students and faculty mentor on how to use various lab equipment.
Machine learning approaches to address problems related to rural hydrology
Significance: Proper understanding of hydrology can help us better manage our water resources and build resilience to hydrologic extremes, such as floods and droughts. New datasets of different hydrologic variables are becoming more readily available with the advances in remote sensing technologies, in situ monitoring, and model-based assessments. Machine learning has great potential in effectively addressing a plethora of problems in the field of hydrology, leveraging these large datasets. Several problems are becoming increasingly more tractable, which was not the case before with limited data availability. This is also opening up several avenues for testing novel hypotheses related to hydrologic process-understanding. Students in this project will be working on machine learning algorithms to address hydrologic problems in the rural settings. The problems can be related to physical process-understanding where, among other things, we try to understand what factors influence different hydrologic processes and how. We study how these processes interact with each other and coevolve. The problem can also be related to hydrologic modeling, where we try to model different aspects of the physical system. Once we have a model of the system, it can be used for a wide range of problems (e.g., generation of forecasts, analysis of future scenarios, optimal water management, etc.).
Student Participation: Students will run machine learning algorithms, for which some preliminary codes will be provided. Students will apply these algorithms to different hydrology problems and analyze the results. Student Outcomes: The projects will involve working with machine learning algorithms applied to a wide range of problems in hydrology. We can also discuss other potential research topics. Prerequisite Knowledge and Training: Introductory course on hydrology or water resources. Some prior experience with coding will be useful.
Significance: Rural bridges are crucial to agricultural economic activities, particularly during harvest seasons when crop yield transportation imposes heavy loads on bridges. Many bridges in rural areas are at or beyond their intended service life and were designed either for unknown or lower vehicle loading than required in modern codes. Unnecessarily imposing load restrictions on bridges leads to increased trip frequencies and lengths for freight vehicles, or demolishing and replacing safe bridges. Therefore, it is desirable to maximize permitted vehicle loading and extend service lives of aging bridges. Reassessing the structural capacity and mechanical response to vehicular loads for rural bridges is critical to achieving this goal. The primary research question that this project addresses is: how does uncertainty in mechanical response to vehicular loads influence structural reliability for rural bridges?
Student Participation: The REU student on this project will conduct analyses to investigate the relationships between uncertain structural characteristics (e.g. composite shear transfer on steel beams designed neglecting composite action) and risk-targeted safe load carrying capacity. The student will propose, conduct, and analyze results for a small experimental testing plan related to the analytical work. Student Outcomes: The student will gain experience performing structural experimental testing and an understanding of 3-dimensional structural system behavior. The student will be introduced to probabilistic concepts for structural engineering evaluation and reliability assessment, advanced mechanical modeling, and machine learning techniques. Prerequisite Knowledge and Training: Mechanics, which is typically acquired by the second year in an engineering program.
Use of Residues from Nebraska Agriculture Sites as Paving Material
Significance: The recycling of waste materials and reducing the carbon footprint of manufactured products through conserving energy and reducing the use of raw materials has become a primary focus. Pavement maintenance and new roadway construction in rural area require tons of new materials. Landfill, as a traditional residue waste disposal method, has a high demand for land resources, which has also become a key issue for solid waste disposal. Recycled asphalt pavement (RAP), asphalt shingles (RAS), waste plastic residues (WPR), agriculture wastes and/or filler by-products can be alternative sustainable materials for asphalt mixture production. The goal of this research project is, first, to determine what are the main types of residues from Nebraska agricultural areas. From this initial assessment, our goal is to identify potential residues that could be used for asphalt mixture production. Our ultimate goal is to determine the most appropriate addition method and percentage of selected residues in the mixture to obtain optimized and feasible asphalt mixtures with recycled material addition.
Student Participation: The REU student will conduct a preliminary survey to obtain data related to residue generation in Nebraska rural areas. The student will collect samples from different residues and perform physical and mechanical characterization of the materials in the UNL/CEE Geomaterials Laboratory. The student will apply the balance mix design to determine optimum material blends (asphalt binder, aggregates, and selected residues) with satisfactory performance results (Superpave Volumetric Design combined with Cracking and Rutting Performance Tests). Student Outcomes: The REU student will be able to conduct primary characterization tests and to understand the material’s physical and mechanical characteristics effects on the asphalt mix design. The REU student will learn how to design an experimental plan to perform asphalt mix design based on the concept of balance mix design, i.e., combining volumetric assessment with performance evaluation. Prerequisite Knowledge and Training: Basic knowledge of excel is recommended but not required. Sampling will be conducted with a graduate student or faculty mentor during the first two weeks.
Resilience of Agricultural Infrastructure and Rural Communities to Natural Hazards
Significance: Despite the criticality of the agricultural industry to both U.S. and global sustainable food production, the resulting lack of economic diversity in most rural areas is theorized to be a major contributor to the low resilience of rural communities to natural hazards, including earthquakes and windstorms. While resilience is a function of many socioeconomic and organizational factors, the disaster response of the built environment is a critical aspect that cannot be ignored. In many rural areas, critical infrastructure includes vital agricultural support and production systems, such freestanding irrigation systems, metal buildings, and grain bins. However, these structures are not typically designed to consistent standards and have been observed to perform poorly in recent severe windstorms. This research aims to generate a fundamental understanding of the performance of irrigation structures during extreme windstorms to enhance rural resilience to natural hazards.
Student Participation: The student will conduct a field survey of irrigation structures and analyze the performance under various recent severe windstorms. Student Outcomes: Student will gain a basic understanding of structural and wind engineering, agricultural infrastructure, and field reconnaissance methods. Prerequisite Knowledge and Training: College-level mechanics/physics, which is typically covered during the first year.
Remote Sensing for Wind Characterization in Rural Areas
Significance: Remote sensing data collection from unpiloted aerial systems (or drones) is an efficient and well-known approach to study the impact following extreme windstorms. Example windstorms include hurricanes, tornadoes, and straight-line winds; which result in damage to both the built and natural environment. Post-event damage surveys typically utilize the Enhanced Fujita (EF) scale to relate structural damage to wind speeds; however, these are limited in application to rural areas. Rural areas, which encompass a significant portion of the US with high windstorm risk, are typically sparsely populated with few structures and consequently, the relationship of natural and agricultural systems to wind speed is highly uncertain. Remote sensing data in terms of high-resolution imagery and point clouds can collect perishable data related to the distribution, orientation, and severity of damage for understanding windstorms. This research aims to develop workflows for analyzing remote sensing data through the application of computer vision and artificial intelligence techniques to understand the wind hazard and response of the built and natural environment with a particular focus on rural areas.
Student Participation: The student will collect and process field remote sensing data related to recent windstorms, inclusive of recent tornadoes, thunderstorms, and the August 2020 Midwest Derecho. The student will also have the option to apply machine learning techniques to extract and characterize features of interest. Student Outcomes: The student will gain an understanding of machine learning techniques and geospatial data in terms of high-resolution orthomosaic images and point clouds as applied for civil engineering, which is typically not taught in undergraduate courses. Prerequisite Knowledge and Training: No formal coursework is necessary. Data collection and processing training will be conducted by a graduate student or faculty mentor during the first two weeks.