New Grants


Branko KerkezBranko Kerkez
Overcoming Social and Technical Barriers for the Broad Adoption of Smart Stormwater Systems;
National Science Foundation; PI–Branko Kerkez
In the age of the self-driving car, what role can autonomous technologies play in improving water systems? Floods are the leading cause of severe weather fatalities across the United States. Furthermore, large quantities of metals, nutrients, and other pollutants are washed off during storm events, making their way via streams and rivers to lakes and costal zones. To contend with these concerns, most communities across the United States maintain dedicated infrastructure (pipes, ponds, basins, wetlands, etc.) to convey and treat water during storm events. Much of this stormwater infrastructure is approaching the end of its design life, which results in more flooding and degraded water quality. Instead of building new and bigger stormwater infrastructure, which is cost prohibitive for many communities, it is possible to use existing infrastructure more effectively. The goal of this proposal is to enable the next generation of smart and connected stormwater systems, which use sensors to anticipate changes in weather and the urban landscape, and adapt their operation using active flow controls (e.g., gates, valves, pumps). This will drastically improve community resilience to floods and water quality. Equipping stormwater systems with low-cost sensors and controllers will provide a cost-effective solution to transform infrastructure from static to adaptive, permitting it to be automated and instantly reconfigured to respond to changing community needs and preferences. This research will address a truly national-scale infrastructure challenge and will lay the foundation upon which to empower and educate communities to adopt smart and autonomous stormwater solutions. The research to enable "smart" stormwater systems will be conducted by a team of engineers, social scientists, computer scientist and environmental experts in tight collaboration with decision makers and citizens across four communities in the United States. The team will close fundamental knowledge gaps to explain (1) to what extent real-time control can improve the hydraulic and water quality performance of individual stormwater sites, (2) how to identify and overcome the barriers that public perception poses to the adoption of smart stormwater systems, and (3) how system-level interoperability can be achieved to guarantee safe and effective performance at the scale of entire communities (100s to 1000s of controlled sites). This will be achieved through three closely coupled scientific objectives, which will include testing of laboratory models of control sites, field-scale water quality studies, the formation of community advisory groups, the analysis of residential surveys in each community, and the stability analysis of system-level control algorithms under various sources of uncertainty. The approach is thus fundamentally motivated around the goal of scalability, as the results will be relevant to many communities across the United States, regardless of their size. By open-sourcing the efforts on and other public forums, the project will also support research capacity-building by reducing the overhead required by others to deploy smart and connected stormwater systems.
Nancy LoveNancy Love
Advancing Cyber-Enabled, Decentralized Water Systems in Rapidly Developing Cities;
National Science Foundation; PI–Nancy Love
This project addresses an enormous global challenge, the management and improvement of decentralized water systems, including treatment and delivery of fresh water as well as removal and treatment of wastewater. The project will provide an in-depth research and training opportunity for twelve graduate students in decentralized water systems in Addis Ababa, Ethiopia. Outside of the United States, most urban water systems are decentralized, meaning they are built after housing is established. In many locations, these systems serve only a segment of an urban area. This project will provide US students a clear understanding of the specific challenges of decentralized water systems through hands-on experiences. Students will develop sensor systems, connected through cellular networks, to form data acquisition networks that can significantly improve the understanding and operation of decentralized water systems. The U.S. students and faculty team will work with five mentors from Addis Ababa University (AAU) who bring significant expertise in context-appropriate, decentralized water infrastructure and expertise. The U.S. students will be paired directly with AAU graduate students, using a peer-to-peer learning model likely to increase the technical impact and cultural exchange impacts of the project. Addis Ababa is excellent site for the proposed research. It is rapidly growing, with a strong existing decentralized water system and strong cellular networks to support the cyber-systems research. In addition, the city has need for more efficiency and further development of the water infrastructure. Strong dissemination efforts are planned that should increase the impact of this project beyond the cohort of participating students. The international team is developing a course on decentralized water systems that will address a significant lack of educational materials on decentralized water systems. In addition, each US student will submit abstracts to a national conference and an on-campus research symposia, and the team proposes to publish on the peer-to-peer learning model as well as papers on the specific research projects.
Jeremy SemrauJeremy Semrau
Methanotrophic-Mediated Methylmercury Transformation: An Important Yet Poorly Understood Biogeochemical Process;
National Science Foundation; PI–Jeremy Semrau
Methylmercury is a very potent neurotoxin produced by some microbes. Once formed, methylmercury can easily bio-magnify, that is, the concentrations of methylmercury in organisms increase as one goes up the food chain. Other microbes have the ability to degrade methylmercury, thus limiting this process of bio-magnification. These known systems of methylmercury degradation, however, do not appear to be significant in most environments. Recent work, however, has found that different microbes, through a process yet to be full elucidated, degrade methylmercury under more environmentally relevant conditions. This process may thus be very important in controlling methylmercury bioaccumulation, and its toxicity. In this project we will delineate this process and determine how wide-spread it may be in the environment. Methane-oxidizing bacteria, i.e., methanotrophs, are widespread in the environment, but their impact on biogeochemical cycling of mercury, has only just been investigated. The investigators have recently found that methanotrophs bind and demethylate substantial amounts of methylmercury (MeHg), a neurotoxic form of mercury that is generated via anaerobic microbial activity. What is remarkable is that methanotrophs do not have merB, encoding for the well-characterized organomercurial lyase, indicating that methanotrophs use an as yet unknown mechanism to demethylate MeHg. Further, methanotrophic-mediated MeHg degradation was observed under environmentally relevant conditions (i.e., nanomolar concentrations of mercury and circumneutral pH), unlike the organomercurial lyase, which is operative only under conditions rarely seen in the environment. As such, it appears that the methanotrophic-mediated MeHg degradation is much more environmentally significant than the canonical merB-mediated pathway. The objectives of this proposal are thus to determine how methanotrophs take up and degrade MeHg. Investigators will examine a suite of methanotrophs that span the phylogenetic and physiological diversity of these microbes as well as several mutants of one of these species to determine how these microorganisms take up and demethylate MeHg, and the impact of MeHg uptake and degradation on growth, activity and transcriptome.
SangHyun LeeSangHyun Lee
Non-Invasive Personalized Normative Messaging Intervention for the Reduction of Household Energy Consumption;
National Science Foundation; PI–SangHyun Lee
Household fossil fuel consumption in the U.S. is responsible for approximately 22% of primary energy consumption and CO2 equivalent emissions. Consequently, it is an objective of this project to identify widely applicable intervention methods potentially capable of promoting environmentally responsible behaviors. The goal of this research is to advance understanding of how personalized normative comparison groups influence the effectiveness of normative feedback interventions through the development and validation of a non-invasive data mining-based behavior intervention framework, with field experiments conducted in homes in Holland, Michigan. Successful implementation of the research would significantly contribute to reducing harmful emissions from the built environment through the enhancement of pro-environmental feedback intervention design by providing a detailed first look into personalized normative feedback. Specific research objectives are: 1) to classify households into several meaningful groups sharing similar consumption patterns on the basis of hourly energy usage data, using non-invasive techniques; and 2) to generate and then empirically evaluate the effectiveness of personalized normative energy use feedback created with the use of readily available consumption data. The research is targeted to provide an in-depth understanding of both the reliability of personalized normative messages, and how and where energy use behavior changes. Further, it seeks to discover long-term effects of personalized normative feedback on household energy consumption and identify the effect of descriptive norm reference groups on energy consumption norm adherence and energy use. All of these could contribute to residential energy use reduction by advancing the theories for normative energy feedback. The findings on how and where personalized normative messaging changes occupant behavior in both the short and long term have important implications for energy reporting, policy making, and meeting of state and national energy reduction goals. In addition, lessons learned on personalized normative messaging on energy use can be readily applied to other pro-environmental behaviors (e.g., water consumption). Particularly, the proposed non-intrusive personalized feedback messaging framework may be capable of wide-scale deployment in advanced utility networks.
Predictive Monitoring and Intervention for Safe Human-Robot Collaboration in Unstructured and Dynamic Construction Environments;
National Aeronautics and Space Administration; PI–SangHyun Lee
The construction industry has the highest number of fatalities and injuries due to hazardous working conditions. The introduction of robots on construction sites has the potential to relieve human workers from dangerous and repetitive tasks by making machines intelligent and autonomous. However, robotic solutions for construction face significant challenges. This project will develop technologies of automated monitoring and intervention through computer vision to provide a means to dramatically improve the perception of construction safety in the presence of co-robots. The new methods developed in this project will impact computer vision, machine learning, and effective human-robot collaboration in unstructured environments, while significantly contributing to safety. Further, the developed methodologies can be broadly applicable in situations where robots are deployed in human-centered environments (hospitals, airports, shipyards, etc.) and have other priorities such as productivity and efficiency as their objective. This project will engage a diverse group of individuals by training graduate and undergraduate students (including women and underrepresented minorities), reaching out to K-12 students, and interacting with industry professionals for broad dissemination of the research results. This research will investigate new computer vision based methods that can be coupled with other sensing modalities for holistic understanding and predictive analysis of jobsite safety on co-robotic construction sites. The project will consist of two main research thrusts. First, holistic scene understanding will be pursued on construction sites using graphical models to enable joint reasoning of various scene components. This holistic understanding in turn will help evaluate compliance with established safety rules expressed as formal statements. Second, predictive analysis will be investigated by exploiting the fact that, for safety intervention, the complex dynamics of a construction scene make it necessary to simulate what will happen next. In particular, Recurrent Neural Networks will be leveraged to predict future events and prevent impending accidents. Finally, an integrated demonstration system will be built and tested on real construction sites.
Jason McCormickJason McCormick
Resilience of Steel Moment Frame Systems with Deep, Slender Column Sections;
Department of-National Institute of Standards & Technology Commerce; PI–Jason McCormick
For a study of the performance during seismic events of deep slender column sections within a steel special moment frame structure (where beams, columns and beam-column connections are designed to be more earthquake resilient).
Krista WiggintonKrista Wiggington
Methods for Measurement of Infectivity and Concentration of Pathogens;
Water Environment & Reuse Foundation; PI–Krista Wigginton
The project will address key gaps in pathogen monitoring methods by incorporating a number of innovative approaches, particularly focusing on norovirus.
Yafeng YinYafeng Yin
Analytical Techniques for Studying On-Demand Shared-Use Mobility;
National Science Foundation; PI–Yafeng Yin
The proliferation of smart mobile devices has given rise to on-demand economy, which aims to effectively bring together consumers and suppliers with very low transaction costs. As a typical example of on-demand economy, ride-sourcing companies, such as Uber and Lyft, are transforming the taxi industry and the way we travel in cities. The companies provide ride-hailing applications that intelligently source private car owners who drive their own vehicles to provide taxi services for profit to riders. These companies have been successful, but have created controversy. This controversy arises due to regulations in terms of price, entry and service quality that are imposed on the taxis while comparatively fewer regulatory requirements have been imposed on ride-sourcing companies. Unfair competition is argued particularly by cab drivers and their employers. The success of ride-sourcing services has thus created doubt about the efficacy of regulating the taxi industry; it challenges the very premise behind regulation in this industry. This grant will develop methodologies and tools for analyzing the structure and competition of taxi markets with ride-sourcing services and deriving insights on their regulation. The grant will provide timely support for the government agencies of many cities to better understand the impacts and implications of ride-sourcing companies and develop policies to guide their deployment. The research results can also shed light on the analysis and management of other types of on-demand economy and other emerging urban mobility services such as car sharing and ridesharing. This grant will involve students at all levels and traditionally underrepresented students, creating interactive, virtual environments for in-class use. Additionally, materials related to an on-demand economy and innovative urban mobility services will be developed to enhance existing courses.
Collaborative Research: Modeling and Analysis of Advanced Parking Management for Traffic Congestion Mitigation;
National Science Foundation; PI–Yafeng Yin
Parking is a growing problem in dense urban areas. To many, finding a parking space in these areas is an unpleasant experience of uncertainty and frustration. Cruising for parking makes traffic on already-congested urban streets even worse and leads to significant waste in time and fuel. In transportation, smartphone-based parking management applications have emerged. These applications help drivers find parking spaces by allowing them to use smartphones to view real-time availability and prices of parking spaces and guide them to open parking spaces, reserved or otherwise. This award develops theoretical foundations and methodologies for analyzing these emerging parking management services. Results from this research provide a better understanding of the impacts of advanced parking management services on parking competition and travel patterns. The research develops policies to reduce traffic congestion and emissions in dense urban areas. This award positively impacts engineering education by offering new materials and case studies and engaging underrepresented student groups in research. Using game-theoretic, dynamic and stochastic programming approaches to investigate both temporal and spatial travel patterns with advanced parking management, this project generates a set of analytical tools that explain the underlying working mechanisms of advanced parking management services and gauge their potential for reducing traffic congestion. The theoretical efforts in this research are complemented by an agent-based simulation, which tests the validity and applicability of the theories, and unveils complex outcomes of parking competition under realistic parking search behaviors. This work advances the knowledge and analysis of parking management and enriches the literature of modeling morning commute and vehicle routing.
Freeway Management For Optimal Reliability;
The University of Florida; PI–Yafeng Yin
This project will develop tools for analyzing and optimizing system reliability on freeways. These tools include analytical and simulation frameworks for the optimization and near real-time performance forecast of active traffic management (ATM) systems. The proposed traffic congestion mitigation toolbox will include local and/or system-wide adaptive ramp metering, integrated ramp-metering and variable speed limit control, hard shoulder running, speed harmonization, dynamic pricing of express lanes, optimized traffic diversions and efficient incident response and management. ATM deployment is a means to meet specific reliability goals below a desirable agency specified threshold. In this project, we will develop a methodological framework to select and optimize appropriate strategies from the ATM toolbox to meet reliability goals. We will also test the validity of the proposed approach using data from a minimum of three freeway facilities in the Southeast region at both rural and urban locations.
Understanding Labor Supply in the Ride-Sourcing Markets;
Didi Chuxing; PI–Yafeng Yin
The nearly $1 million, three-year joint research program focuses on transportation optimization, big data, artificial language learning and artificial intelligence. Among the goals are better understanding transportation-behavior norms, reducing congestion on the global transportation infrastructure, and providing solutions to mobility.

2016 Grants

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