Rural areas, which contain approximately 20% of the US population (49 million people) and 80% of the land area in the United States are fundamental to human well-being in both rural and urban areas. Within the United States, rural areas provide unique resources such as the infrastructure for food and bioenergy production as well as the transportation infrastructure from inland urban centers to ports. Despite this, little attention is paid to the unique challenges and opportunities these areas face with respect to building and maintaining civil infrastructure.
In this ten-week summer research program, students will work with faculty and graduate students in the Department of Civil Engineering to conduct research and 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 engineering industry.
Competitive stipend: $4,500
Double-occupancy 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
Contaminant Behavior in Groundwater under Climate Change
In rural areas, groundwater is an important resource for drinking water and irrigation. In the future, changing climates may have significant impacts on land use and soil conditions in agricultural areas. In this project, we will investigate how agricultural contaminants including steroid hormones, antibiotics and pesticides behave in soil under scenarios of climate change. The REU student will assist a graduate student with column experiments to investigate the influence of variables such as temperature, organic carbon, and soil texture on contaminant movement through soil.
Connecting Refuse Truck Fuel Consumption and Tailpipe Emissions to Vehicle and Trip Characteristics
Fuel consumption and emissions are known to be correlated. Fuel consumption is also correlated with characteristics of traffic and roadways. For this project, fuel consumption, emissions, traffic, and roadway data were collected with various on-board sensors for refuse trucks with varying vehicle characteristics. These data will be analyzed through use of multivariate analysis. Results from this analysis should better define the relationship between emissions and fuel consumption in terms of vehicle trip characteristics.
Fuel consumption and emissions are known to be correlated. Fuel consumption is also correlated with characteristics of traffic and roadways. For this project, fuel consumption, emissions, traffic, and roadway data were collected with various on-board sensors for refuse trucks with varying vehicle characteristics. These data will be analyzed in a manner similar to that done by Evans et al (Evans, 1976) through use of multivariate analysis. While simplistic in its nature, the lack of knowledge regarding emissions and fuel consumption characteristics of solid waste collection vehicles suggests beginning with basic efforts to characterize fuel consumption and emissions as a function of vehicle and trip characteristics. The characteristics of interest include the truck specifications as well as vehicle speed, acceleration, distance traveled by functional type of roadway, idling time, stopped time (including idling time), travel time, and number of stops. Results from this analysis should better define the relationship between emissions and fuel consumption in terms of vehicle trip characteristics. It is expected that these results may then be used to help formulate operating strategies to help reduce fuel consumption. Reducing fuel consumption benefits the solid waste collection operator through cost savings and benefits society through reduced emissions.
Multiscale Experiment-Simulation to Identify Key Material Properties for Sustainable Rural Infrastructure Systems
This project will enhance fundamental understanding of the material properties and fracture characteristics of individual phases in heterogeneous mixtures that are often used in transportation infrastructure such as roadways, rails, and airfields. In this project, the REU participants will be primarily involved in performing multiscale experiments necessary to characterize material properties and fracture characteristics of the mixture components (or phases) and interfaces between components.
. In particular, we are interested in optimizing key design variables of the overall infrastructure performance in rural conditions that are quite different from key factors considered for urban infrastructure systems. This research effort will be based on our previous research and will incorporating several advanced features, including moisture- and temperature-dependent material characteristics, nonlinear-inelastic cracking at different length scales, multiscale test methods, and multiscale computational models. Project outcomes are expected to dramatically improve our ability to assess the effects of individual mixture components and their interactions on the overall infrastructure performance, so that safer and more economical design of mixtures and infrastructures can be achieved. The proposed approach is transformative because it is expected to increase the ability to predict damage-dependent behavior and the service life of an entire rural infrastructure systems while minimizing testing effort and producing more accurate predictions than current phenomenological approaches that are mostly based on previous observations, experimental data, and/or forensic analyses.
Development of best management practices to limit transport of antimicrobials and antimicrobial resistance genes
The goal of this research project is to understand the fate and transport of AMs and AMR genes in the agricultural environment and develop best management practices (BMPs) to control their proliferation. In the proposed project, the student will work with faculty and graduate students in analyzing the AMR genes in runoff and soil samples. The student will learn how to use molecular techniques to measure AMR genes in environmental samples as well as discover how different management practices may influence the fate and transport of AMs and AMR genes in the environment.
The extensive use of antimicrobials (AMs) in the livestock industry for animal disease treatment/prevention and growth promotion has promoted the emergence of antimicrobial resistant bacteria. Antimicrobial resistance genes (AMR genes) - the genetic materials that render resistance mechanisms to bacteria - can proliferate among the bacteria in the environment. If human pathogens acquire AMR genes and become antimicrobial resistant, antibiotic treatment will lose its effectiveness in treating infected individuals (Walsh 2000). The goal of this research project is to understand the fate and transport of AMs and AMR genes in the agricultural environment and develop best management practices (BMPs) to control their proliferation. In the proposed project, the student will work with faculty and graduate students in analyzing the AMR genes in runoff and soil samples. These samples will be collected from a rainfall simulation test that a collaborator will conduct in the field. Through this project, the student will learn how to use molecular techniques to measure AMR genes in environmental samples. The student will also discover how different management practices may influence the fate and transport of AMs and AMR genes in the environment.
Green storm water management system for rural communities
For rural community sustainability, it is important to understand how EIS will promote the removal of contaminants. The research questions of this project include (1) how different porous media will promote contaminant removal in EIS, (2) how the intermittent nature of the storm events will influence contaminant removal in EIS.
Although storm water management has traditionally focused only in urban area, increased attention is necessary for rural communities during agricultural development and urbanization. Urbanization results in a significant rise in the volume of storm water runoff due of the increase in impervious surfaces in cities which prevent the storm water from infiltrating the subsurface and reaching the water table. The runoff can also pick up contaminants, such as manure wastes and fertilizers, from the surfaces and eventually enter natural aquatic systems, hence polluting the impacting the ecosystems. Engineered infiltration systems (EIS) are structures in urban areas that contain porous filtration media for the infiltration of stormwater to reduce the volume of surface runoff. Typically, the design of EIS focuses on the fast infiltration of stormwater, and the removal of contaminants in the stormwater was not always considered. For rural community sustainability, it is important to understand how EIS will promote the removal of contaminants. The research questions of this project include (1) how different porous media will promote contaminant removal in EIS, (2) how the intermittent nature of the storm events will influence contaminant removal in EIS.
This project represents a transformative shift in the methodology used to manage bridge infrastructure and rationally extend service life by transitioning toward a performance-based paradigm. The specific research tasks for the REU student could include data collection and processing (mining) from disparate sources; collaboration with faculty in computer, social, and economic sciences to characterize loads and correlate available data to representative loading representations; modeling and parametric sensitivity studies of bridge structures using finite element analysis; assisting with deployment, data collection, and post-processing of sensor output from bridge tests; developing models to correlate sensor data to physical performance, and performing sensitivity studies to investigate optimum reliability and proposed metrics for asset management and decision-making.
Extending infrastructure service life is of particularly significance for sustainability of rural infrastructure, where construction activities become increasingly costly with increasing distance from larger population centers and associated manufacturing capabilities. Structural reliability addresses the relationship between two fundamental components: the load applied to a structure, and the capacity of the structure to support loads. Both of these aspects have been characterized probabilistically for general use and codified for application in the practice of structural engineering. Rural bridges are evaluated in the same manner as typical highway bridges, despite the fact that the loading characteristics for “off-system” rural bridges are markedly different from typical highway loads, with much lower traffic rates, yet periodically high loads from farm equipment and agricultural freight. To address the objectives of this project, data will be compiled from various sources, including surveys, selected weigh-in-motion records, and data mining of social and economic drivers to provide a targeted representation of the loads applied to individual rural bridge structures along transportation routes and corridors. Additionally, parametric studies will be performed on representative bridge structures with various configurations, element geometric and material characteristics, and boundary conditions. Complementary sensor datasets obtained from bridge tests will be used to validate modeling capabilities and demonstrate correlation of bridge behavior and performance characteristics. The results of the parametric studies and the corresponding sensor data will be incorporated into artificial neural network models to correlate sensor data to structural characteristics and enable more refined and reliable assessment of bridge capacities by end-users and owners.
In this project, the REU participant will work with faculty and graduate students to conduct a case study analysis, including both safety and mobility elements, for a location within Nebraska. Safety analysis will be conducted obtaining crash history data for the selected corridors, with potential impacts of road diet geometrics determined based on Highway Safety Manual procedures. Mobility analysis will focus on traffic microsimulation of the selected areas in question, examining the impacts to travel time that the proposed changes may be expected to impart. The take-away for the REU participant will be experience with both safety and mobility research applications within transportation, as well as expertise with both drafting and traffic simulation software applications.
A recent trend in the field of transportation engineering is the recognition that more does not always equal better. A road diet is the process of reducing capacity, usually in the form of reducing the number of lanes, along a corridor to prioritize secondary modes of travel or increase safety. Road diets have been successful in two different conditions - improving safety for lower-trafficked conditions, and improving multi-modal access in higher-trafficked conditions. For high-traffic areas, converting a four-lane road down to two lanes may provide additional parking, bike lanes, and/or pedestrian access, benefiting local business. For low-traffic areas, safety benefits are seen when reducing a four-lane roadway without auxiliary turn lanes down to a three-lane roadway including a dedicated center turn-lane refuge. The purpose of the current research is to focus on low-volume conditions, examining the history and potential of these applications within Nebraska. Examining Nebraska municipalities with fewer than 12,000 residents, there are currently 14 Class 1 and Class 2 communities in which the major roadway passing through town expands from a two-lane roadway to a four-lane roadway. These roads create routine safety hazards in the form of rear-end crashes when left-turning vehicles are stopped in a lane with through traffic. The prevailing design choice within transportation engineering has shifted since the construction of these roadways to prefer a three-lane section, providing one lane of through traffic in each direction with a left-turning refuge in the center lane.
This project involves the development of “smart infrastructure” system(s) that capitalize on past health monitoring successes (e.g. bridges, buildings, pavements, intelligent transportation systems) to effectively interpret and manipulate Big Data to eliminate bridge failures, increase structure durability and save lives. In this project, the REU participant will assist with sensor development and deployment and will work with graduate students on the development of models using laboratory and field data. REU participants will be integrated into all aspects of the work, from understanding how models used to predict response under varying loads over varying timeframes are developed and updated to reflect changes in bridge “health” to being actively involved with laboratory and field investigations of the response of single bridges and groups of bridges to simulated and actual demands.
Successful development and deployment of smart infrastructure system(s) will drastically change the way transportation infrastructure is managed and how resources are allocated. In this research project, we will investigate which sensors and sensing systems are appropriate for smart infrastructure; what component and system models need to be developed and how component and system degradation be adequately addressed. The research project will incorporate sensor development and deployment and application of statistical and physical models to develop smart infrastructure systems that will be evaluated using laboratory and field testing. Sensor development will focus on producing innovative, advanced, low cost, integrated systems. Statistical models used will focus on development of smarter and faster algorithms that improve upon existing updating and prognosis tools and on enhanced, real-time integration of various data sets. Physical models will be used to predict system responses and the development of advanced, time-dependent constitutive relationships. In this project, the REU participant will assist with sensor development and deployment and will work with graduate students on the development of models using laboratory and field data. This project specifically addresses rural sustainability by providing a mechanism to reliability understand and quantify rural bridge condition in real time, thereby making those structures last longer and require less raw materials for their repair and replacement. Quantifying performance of raw materials used in the construction rural bridge networks via the development and deployment of the proposed “smart infrastructure” system helps establish how those materials can be more effectively managed and integrated into designs. The outcomes of this project will fundamentally change the way bridges, one of the most vital components in our transportation network, have been designed, implemented, assessed and managed.