Rural areas, which contain approximately 20% of the US population and over 90% of the land area in the United States, are fundamental to human well-being in both rural and urban areas. Rural areas provide resources such as the infrastructure for U.S. food and bioenergy production as well as the transportation infrastructure from inland urban centers to ports. Rural areas are characterized by agricultural- and natural resource-based economics, stable or declining populations with low population densities, and “farm-to-market” localized transportation patterns, and these characteristics necessitate new technologies and approaches for civil infrastructure. Despite the differences between rural and urban regions, little attention is paid to the unique challenges and opportunities for sustainability in rural areas.
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: $6,000
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
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.
Characterization of Gas Production and Mechanical Properties of Solid Waste in Rural Areas
Significance: Landfills are typically sited in rural areas with low population densities. Gas production and leachate can be particularly concerning in rural areas due to the reliance upon groundwater. Therefore, accurate predictions of landfill gas (LFG) emissions and waste settlement are crucial for the prevention of greenhouse gas emissions and for sustainable management of a municipal solid waste (MSW) landfill. The objective of this research is to characterize gas production and leachate of solid waste by using a direct injection logger including a piezocone penetration test (PCPT) with a hydraulic profiling tool (HPT) and membrane interface probe (MIP). This project aims to evaluate the properties of landfills and determine best practices for sustainable management of gas production. The primary research questions to be answered in this project are: 1) Can gas production be accurately measured in landfills using an in situ method? and 2) What are the in situ mechanical properties of solid waste?
Student Participation: As part of this project, the student participant will collect soil properties and gas emission data using PCPT with MIP and HPT from an operating landfill in Butler County, Nebraska, a rural county with a population of 8,000. Student Outcomes: The student will learn how to survey soils and measure gas emission using state-of-the-art technologies and conduct statistical analysis of data. Prerequisite Knowledge and Training: No formal prerequisites, training on using the measurement equipment will be provided by the faculty mentor and graduate student mentor during the first two weeks.
Challenges and Potential for Electric Vehicle Adoption in Rural Nebraska
Significance: The transportation sector is among the largest contributors of greenhouse gas emissions in the United States. A key pathway to decarbonization of the sector is electrification of the private vehicle stock. This pathway is particularly important in rural areas, where transit and land use planning are less feasible options. Rural residents generally drive further than their urban counterparts, while facing a relative deficit in infrastructure investments. As such, there is a need for an analysis of both the sufficiency of charging infrastructure in rural regions and the ability of electric vehicles to meet rural residents’ travel needs. The primary objective of this research is to examine the ability of electric vehicles (EVs) to satisfy the travel requirements of rural Nebraskans. In addition, the spatial allocation of charging stations relative to travel demand will be investigated to identify whether there is a rural deficit.
Student Participation: The REU student will collect data on the location of EV charging stations in Nebraska and analyze travel demand data from the national household travel survey (NHTS) and other sources. The REU student will perform basic analysis in GIS and other software to visualize patterns. There may be an opportunity to perform a survey of rural residents, depending upon available time and student interest. Student Outcomes: The REU student will gain knowledge of transportation planning and data analysis. They will also learn GIS and data science skills in R and/or Python. Prerequisite Knowledge and Training: No prerequisite knowledge necessary. The faculty mentor will provide training in GIS and data science software.
Dr. Seunghee Kim
Department of Civil and Environmental Engineering: Geotechnical Engineering
Influence of Nebraska biochar on the hydraulic and mechanical properties of rural soils
Significance: Biochar, a product of combustion of organic materials, such as corps, rice husk, forest residues, and agricultural residues, has been emerging as a potential soil amendment. To date, there are many kinds of researches that examined the potential impacts of biochar on soil carbon sequestration capacity, soil fertility, crop production, and chemical properties. However, a study on the implication of biochar application on the hydraulic and mechanical properties is still lacking. Research on this aspect could open a new opportunity for biochar use and management, particularly for rural soils. The research objective is to examine a potential improvement in the hydraulic and mechanical properties of rural soils in Nebraska.
Student Participation: The REU student on this project will conduct various laboratory tests to examine hydraulic and mechanical properties of rural soils that are amended by local biochar. The mechanical properties include bulk density, compaction, porosity, stiffness, and shear strength. The hydraulic properties include water infiltration, saturated hydraulic conductivity, and water retention. Student Outcomes: The student will gain experience in conducting various geotechnical index, hydraulic, and mechanical tests. The student will be introduced to the effective compilation and analysis of experimental data. Prerequisite Knowledge and Training: It is required that the student has taken an introduction to geotechnical engineering class (or equivalent) before the suggested research work.
Dr. Xu Li
Civil and Environmental Engineering: Environmental Engineering
Producing Clean Energy and Value-Added Products from Animal Wastes
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 animal 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
Predicting Rural Environmental and Water Quality Under a Changing Climate
Significance: Agricultural nonpoint source pollution (NPS) is a significant contributor to the contamination of surface water and groundwater resources. With increasing demands on global agricultural production and the need to maintain sustainable water resources in the future, it is crucial to identify areas with high agricultural NPS potentials. Understanding the spatial distribution of NPS pollution is essential for the design of mitigation strategies. This project will quantify and predict the spatial distribution of agricultural NPS risks in the United States under historical and future climate scenarios.
Student Participation: This year, the REU student’s project will focus on identifying the factors that impact contaminant concentration in groundwater. The student will collect necessary data from online database. The student will find correlations between various nature and human factors and contaminant concentration. Student Outcomes: Student will gain an introductory understanding of agricultural non-point source pollution in the environment. Student will develop expertise in literature review, data mapping, and relevant software. Prerequisite Knowledge and Training: Student should have a basic understanding of water chemistry and mass balance analysis. Use of the modeling software (e.g. ArcGIS) will require training, which will be guided by the faculty advisor and the graduate student mentor.
Multilevel Analytics and Data Sharing for OPerations Planning (MADS-OPP)
Significance: Changes in infrastructure condition, such as degradation to a bridge element that reduce load carrying capacity, can cause costly rerouting delays. MADS-OPP will assess transportation infrastructure conditions to identify optimal maneuver routes in real time. The project paves the way for bringing Big Data opportunities to bridge health assessment using next generation sensing techniques and platforms. Our technology will facilitate controlled sharing of data from different owners, allow distributed data storage, and audit data streamed from multiple sources. The project will demonstrate how algorithm-based use of complex data sets can model infrastructure health at element and wholistic levels. We will develop data products using physics driven, high-fidelity machine learning algorithms and visualizations for infrastructure condition assessment in support of optimal route planning.
The primary questions we are addressing are: Can durable, low-cost sensing systems be integrated with Big Data pipelines to automate civil infrastructure health monitoring processes to provide advanced warning of structural deficiencies that could be a concern (e.g., cracks, spalling, corrosion, material degradation, superstructure, and substructure deterioration)? Can extreme natural or human-made demands that cause deficiencies (e.g., tornados, hurricanes, earthquakes, high-wind events, fire, scour, vandalism, impact, and blast) also be identified?
Student Participation: The student will support laboratory and field testing and modeling of rural bridges and reduce/examine collected field data in support of advanced filtering and neural network techniques for structural health monitoring systems. Student Outcomes: Student will gain an understanding of structural engineering, bridge structures, instrumentation, field testing, and data manipulation techniques. Prerequisite Knowledge and Training: College-level mechanics/physics, which is typically covered during the first or second year of an engineering program. Statistics skills would also be helpful. Field testing, instrumentation and data reduction and manipulation training will be conducted by a graduate or post-doctoral student mentor.
Dr. Mojdeh Pajouh
Department of Civil and Environmental Engineering: Geotechnical and Materials Engineering
Study of Electric Vehicles Crashworthiness and Compatibility with Existing Roadside Features
There has been considerable interest in electric vehicles (EVs) from consumers, government agencies, and manufacturers resulting in an exponential growth in new EV sales. EV-related run-off-road (ROR) crashes and interactions with roadside features can pose unique and significant concerns. It is imperative that infrastructure be compatible with all vehicles to avoid unnecessary loss of life and critical transportation infrastructure damage. However, little is known about EV crash performance with roadside features. The objective of this research is to review real-world crash data involving EVs with roadside features and guide transportation agencies to implement the optimal deployment of safe products, research, and interactions. This research aims to identify (1) what EV and roadside feature compatibility problems exist, (2) what are contributing factors to “bad outcomes” involving EVs and roadside features, and (3) provide recommendations to improve the universality of roadside feature design.
Student Participation: Collect crash data from state DOTs involving EVs, and plot comparative statistics. Identify factors which contributed to the EV crash. Determine if specific roadside features, impact conditions, driver actions, roadway and roadside geometries, or EV makes, and models are correlated to increased risk of “bad outcomes”. Student Outcomes: Student will learn (1) how to collect and analyze crash data and extract patterns and, (2) learn about electrical vehicles technologies and their crashworthiness, a much-needed topic for emerging new technology. Prerequisite Knowledge and Training: Basic Knowledge in vehicle dynamics and structural analysis, data analysis, and statistics.
Dr. Grace Panther
Civil and Environmental Engineering: Environmental Engineering and Engineering Education
Spatial Visualization Skills and Engineering Problem Solving
Significance: Spatial skills have been linked to success in STEM degree attainment. Spatial skills have also shown some correlation to successful problem solving. This study investigates the links between spatial skills and problem solving by using several spatial measures and engineering problems while collecting eye tracking data and perceived stress (wrist band data). Two research questions guide the project: 1) Do rural and urban students differ in terms of their spatial skills and engineering problem solving? 2) Do stress levels and eye movements differ between rural and urban students when solving engineering problems?
Student Participation: The student will conduct quantitative data analysis using already collected eye tracking and wristband data. The student will have the autonomy to lead the direction of the data analysis as they interpret results. Student Outcomes: Student will gain basic understanding of the field of engineering education research which is not typically discussed in undergraduate courses. Prerequisite Knowledge and Training: No prerequisite knowledge necessary. Training on engineering education research methods and analysis will be conducted by the faculty mentor. Basic Excel skills are preferred to assist in data cleaning and analysis.
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.
Computer-Vision Based Health Monitoring of Aging Rural Bridge Infrastructure
Significance: The number of aging rural bridges are increasing in Nebraska. When it comes to make decisions to repair, rebuild, or rehabilitate these aging rural bridges, decisions are made by prioritizing the ranks of these bridges. Condition ratings made by the inspectors for bridge deck, superstructure, and substructure are one of the parameters used in this decision making. Human visual inspection is typically conducted first and if needed, additional measures are used to assess the level of deterioration for condition ratings. This process becomes a challenge when there are thousands of bridges and limited number of inspectors available. To assist this inspection process, this project will focus on developing a computer-vision based system to monitor the health of our aging rural bridge infrastructures.
Student Participation: The REU student will assist with collecting data, labeling images, detecting features using state-of-the art machine learning and deep learning algorithms, and creating a quantitative database for tracking temporal and spatial changes to monitor the progression of damage deterioration. Student Outcomes: The student will gain an understanding of machine learning and computer vision. Prerequisite Knowledge and Training: No formal coursework necessary.
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.
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 steel grain bins. However, these structures are not typically design 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 steel grin bins during extreme windstorms to enhance rural resilience to natural hazards.
Student Participation: The student will conduct a field survey of constructed steel grain bins 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.