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Dimitrios ZekkosDimitrios Zekkos
Characterization of Landslides & Rock Mass Strength Leveraging the 2015 Mw 6.5 Lefkada Earthquake in Greece;
United States Geological Survey; PI–Dimitrios Zekkos
Landslides represent one of the most significant hazards during and in the immediate aftermath of an earthquake. On December 17th 2015 an Mw 6.5 event occurred in Lefkada island in Greece. During the earthquake, essentially the entire west coast of the island, extending many tens of km failed catastrophically. The research team deployed a fleet of Unmanned Aerial Vehicles (UAVs) just two days after the earthquake as well as five months after the earthquake mapping a plan view area of 6.5 km2. In addition to pre-earthquake topography, the team was provided access to pre- and post- event high-resolution satellite imagery. Both satellite and UAV imagery will be used to create a detailed polygon landslide inventory and more interestingly, create three-dimensional topography of the landslides. This study aims to: (a) Analyze a unique 2D and 3D depository of landslides and make comparisons between the landslide data generated from two different state-of-the-art technological mapping tools: the UAAV-enabled ultra-high definition imagery and high resolution satellite imagery. High resolution topography and imagery data will allow for careful documentation of the landslide areas, and also of the volume of the landslides thanks to the nearly immediate mapping of these landslides. (b) To examine the landsliding patterns caused by the 2015 earthquake and compare them to the landsliding inventory from the 2003 Lefkada earthquake with emphasis on the geomorphic evolution of the Lefkada slopes and the consequences in stability of rock masses. (c) Leverage the unique depository to derive rock mass strength estimates. The anticipated outcome of this research is a well-characterized landslide event with respect to regional landslide distribution, landslide 3D geometry and rock strength. Dimitrios Zekkos
Adda Athanasopoulos-ZekkosAdda Athanasopoulos-Zekkos
Integrated Field & Laboratory Based Assessment of Liquefaction Triggering & Residual Strength of Gravelly Soil;
National Science Foundation; PI–Adda Athanasopoulos-Zekkos
One of the leading causes of damage during earthquakes is soil liquefaction. Liquefaction causes loose soil to behave more like a heavy liquid than a solid which can result in settlement of buildings as well as landslides and dam failures. Recently, extensive gravel liquefaction and damage was observed in the 2008 Wenchuan, China, earthquake and the 2014 Cephalonia, Greece, earthquake sequences, and the evidence indicates that gravelly soils that were considered non-liquefiable using existing liquefaction susceptibility methods, did liquefy. This study will provide a fundamental understanding, as well as practical guidance on assessing whether a gravel deposit will liquefy. Such deposits are encountered widely and their potential liquefaction is commonly a critical consideration in the stability of our nation's ports, dams, levees and in general, civil infrastructure. The improved procedures that will be developed will enhance the resiliency of these facilities, and will help in avoiding unnecessary expenses when gravel liquefaction is not really an issue, or more importantly, highlight the need for stabilization when gravel liquefaction is indeed expected. The research team will establish a web-site with a gravel liquefaction database, and document all available case histories of gravel liquefaction. Videos on the mechanism of liquefaction that can be used for outreach purposes will be developed, as well as a recorded webinar and other educational resources on the outcomes of this study with emphasis on the recommended procedures for liquefaction assessment of gravelly soils. Characterization of gravelly soils in a reliable, cost-effective manner for routine engineering projects remains a challenge and methods such as the Cone Penetration Test and Standard Penetration Test are not considered appropriate. Even for large projects, such as dams and energy projects, characterization is expensive (if Becker testing is used) and problematic. Nevertheless, because liquefaction is known to have occurred in gravelly soils in a significant number of earthquakes, dam engineers are frequently called upon to assess the potential for liquefaction in gravels and liquefaction mitigation costs often run into millions of dollars. With the majority of research in soil liquefaction engineering focused on the assessment of the likelihood of "triggering" of liquefaction in sands and silty soils, new approaches, and transformative methods for characterizing gravels are needed. In this collaborative project, there is a coordinated plan that combines unique large scale laboratory testing and innovative field testing at key gravel liquefaction sites to develop liquefaction triggering charts and post-liquefaction shear strength recommendations for gravelly soils. A prototype 300-mm diameter cyclic simple shear (CSS) device has been developed at the University of Michigan and will be used to evaluate the cyclic (and monotonic) undrained shear response of gravelly soils in the laboratory. A next-generation dynamic cone penetration test (DPT) that was originally developed in China will be modernized with key instrumentation and will be used for field testing along with in-situ shear wave velocity (Vs) measurements. Field testing will be conducted in the USA, China, and Greece, leveraging resources from international partners, in-situ testing firms and organizations. The PIs will leverage well-documented case studies of gravel liquefaction in the recent Cephalonia earthquake to validate the new methods and augment the field performance record of gravel liquefaction.
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.
Lutgarde RaskinLutgarde Raskin
Optimizing Filter Operation in an Ozone-Biofiltration Plant to Reduce Selection for Opportunistic Pathogens in Drinking Water Production;
City of Ann Arbor; PI–Lutgarde Raskin
Non-tuberculous mycobacteria (NTM), some of which are opportunistic human pathogens that can cause disease in immunocompromised individuals, are frequently detected in drinking water. There is growing evidence that drinking water disinfectants select for NTM. An important, but mostly unexplored area is the selection for NTM during biofiltration, which has become a popular technology in centralized drinking water treatment plants (DWTPs) in North America. Few if any studies have evaluated the impact of disinfectant exposure during backwashing on the selection for NTM in biologically active filters. We hypothesize that reducing disinfectant exposure of the microbial communities in BAC filters promotes more diverse biofilm communities with microbial populations that effectively outcompete pathogens, while achieving the same or better filtration performance. We will evaluate this hypothesis using a combination of full-scale and pilot-scale investigations at the Ann Arbor DWTP, with culture-independent, high-throughput microbiology. Specifically, we propose to test the impact of dechlorinating the backwash supply on filter microbial communities, focusing on whether this strategy reduces NTM levels. This research will result in strategies for utilities to reduce the levels of disinfectant-resistant, opportunistic pathogens in filters and thereby lower the likelihood of seeding the distribution networks with these microbes. While our work will focus on the Ann Arbor DWTP, it will be applicable to other utilities that practice biofiltration, especially those that pre-ozonate and use disinfectants in their backwash water.
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.
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.
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.
Herek ClackHerek Clack
Development and Testing of a Prototype Non-Thermal Plasma Device to Prevent Airborne Transmission of Infectious Diseases to and from Livestock;
Michigan State University; PI–Herek Clack
A lab-scale non-thermal plasma device has been proven to achieve greater than 99% inactivation of an airborne viral surrogate in in vitro testing. The objective of the present proposal is to design and construct a larger, pilot-scale unit whose size, capacity, and design flexibility will allow it to be used for in vivo testing with live animals in either an actual farm environment (e.g. pork or poultry production facilities) or the more controlled conditions of an animal testing laboratory. Following in vitro testing of the lab-scale device (both the successful ongoing tests using a viral surrogate and scheduled tests using a live swine virus), the development of a larger unit of flexible design is critical to obtaining the live-animal test results needed to begin product commercialization.
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.
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).
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