National Science Foundation; PI – Nancy Love

Water in modern society is inherently complex; its management, use, value, and accessibility are all defined through a complex “system-of-systems” web of technology, regulations, policy, economics, public health, social history, and human perceptions. Furthermore, every type of water in water systems (e.g., source and receiving waters, treated drinking water, the various forms of water containing waste materials from human use, stormwater, groundwater, irrigation water, industrial water) is connected to and impacts multiple other types of water the flows through our communities. This proposal is grounded in the premise of One Water. Among the many features of this approach is the acknowledgement that waste management is the first step in effective water treatment for both potable and non-potable uses. Our vision for this proposal is to normalize source separation as a strategy to create sustainable, resilient, and equitable water systems.

DISES: Governing Green Stormwater Infrastructure for Just and Adaptive Urban Flood Management

RAND Corporation; PI – Branko Kerkez

The scope of work for our research encompasses several key objectives that are aimed at addressing issues related to stormwater management and flooding.

Collaborative Research: Merging Human Creativity with Computational Intelligence for the Design of Next Generation Responsive Architecture

National Science Foundation; PI – Evgueni Filipov

The core hypothesis of the proposed work is that human creativity can both lead and be led by computational intelligence in the form of machine learning to develop designs of engineered systems that are measurably superior both artistically and functionally. It is further hypothesized that the emerging fields of origami-inspired adaptive structures and responsive architecture can distinctly benefit from combining human and computational intelligence due to the unique challenges in developing sufficient computational representations and the complex and competing design objectives required. This work intends to establish a new strategy to integrate architectural design processes and human-derived designs with physics-guided machine learning and computational design optimization, and to evaluate this strategy on the application of origami-inspired responsive architecture. To accomplish these goals, the proposed technical approach will involve an integrated computational experimental research effort with three thrusts: 1) Establish a coupled human-computer design strategy for component-level origami-inspired adaptive structures to simultaneously meet multiple external environmental interaction goals and intrinsic structural requirements; 2) Create a computational framework to evaluate the performance of origami-inspired adaptive structures, and augment their design based on human input and computer guidance; and 3) Establish a framework for the introduction of new structural concepts in an architectural design process and application of this framework to identify emergent origami-inspired concepts for adaptive structures.

CLIMA: Nimble, Adaptive, and Reusable Structures (NARS): Systems, Mechanics, and Financing

National Science Foundation; PI – Evgueni Filipov

The objective of this project is to establish a comprehensive framework for system integration, structural mechanics, and financing for nimble, adaptive, and reusable structures (NARS). The overarching idea behind NARS is to harness emerging concepts in origami-inspired deployable and reconfigurable structures for strategic, widespread and commercially viable applications in building infrastructure. The systems can be potentially transformative for climate change mitigation and adaptation offering distributed fabrication, nimble and rapid assembly for post-disaster recovery, geometrically adaptable modules, and reusable building assets.

Development of WRRF Influent and Plant-Wide Simulation Tool

Great Lakes Water Authority (GLWA); PI – Glen Daigger

The Great Lakes Water Authority (GLWA) Water Resource Recovery Facility (WRRF) provides primary and secondary treatment, chemical and biological phosphorus removal, and disinfection of municipal, industrial, and combined sewer flows using conventional primary treatment, with ferric chloride addition followed by high purity oxygen (HPO) activated sludge process. Primary and secondary sludge is thickened in separate gravity thickener complexes, combined, and either processed into pellets by centrifuge dewatering and thermal drying or dewatered and incinerated. University of Michigan (U-M) researchers have been assisting GLWA with analysis of the functional treatment mechanisms and performance of the WRRF since 2017, including investigations of chemical and biological phosphorus removal, the fate of phosphorus through the WRRF, opportunities for phosphorus recovery, and development of a whole plant model in SUMO. GLWA wishes to continue to receive assistance from U-M researchers and to provide research opportunities for U-M students. The purpose of this project is to provide the basis for continuing this collaboration and to conduct research, as agreed to by GLWA, to further assist with understanding and optimizing the performance of the WRRF.

Project M (SEWRB-UM): ELC SEWER Network Project-Wigginton/Eisenberg

State of Michigan, Health and Human Services Department ; PI – Krista Wigginton

This project provides the State of Michigan with data on wastewater SARS-CoV-2 levels in wastewater treatment plants. Samples will be collected regularly and SARS-CoV-2 genes will be quantified with digital droplet PCR.

Assistance Optimizing the Great Lakes Water Authority (GLWA) Water Resource Recovery Facility

Great Lakes Water Authority (GLWA); PI- Glen Daigger

The Great Lakes Water Authority (GLWA) Water Resource Recovery Facility (WRRF) provides primary and secondary treatment, chemical and biological phosphorus removal, and disinfection of municipal, industrial, and combined sewer flows using conventional primary treatment, with ferric chloride addition followed by high purity oxygen (HPO) activated sludge process. Primary and secondary sludge is thickened in separate gravity thickener complexes, combined, and either processed into pellets by centrifuge dewatering and thermal drying or dewatered and incinerated. University of Michigan (U-M) researchers have been assisting GLWA with analysis of the functional treatment mechanisms and performance of the WRRF since 2017, including investigations of chemical and biological phosphorus removal, the fate of phosphorus through the WRRF, opportunities for phosphorus recovery, and development of a whole plant model in SUMO. GLWA wishes to continue to receive assistance from U-M researchers and to provide research opportunities for U-M students. The purpose of this project is to provide the basis for continuing this collaboration and to conduct research, as agreed to by GLWA, to further assist with understanding and optimizing the performance of the WRRF.

Transforming Equilibrium Analysis Paradigm for Modeling Transportation Networks with Intelligent Traveling Agents

National Science Foundation; PI- Yafeng Yin

Transportation network equilibrium modeling paradigm plays an important role in planning and operations of transportation networks. The paradigm has been established via a “bottom-up” approach over the past 66 years. The proposed research aims to transform this “bottom-up approach” by developing an end-to-end learning and optimization framework. Our research is also motivated by observing that, assisted by advanced decision-support technologies, future traveling agents (connected drivers or automated vehicles) will possess strong learning and computation capability, and their travel decisions can be algorithmic, strategic and adaptive. The presence of these intelligent agents cast doubt on whether the equilibrium modeling paradigm is adequate for planning future urban transportation networks, and whether the notion of equilibrium remains relevant for capturing the interactions of intelligent traveling agents. The proposed research consists of two thrusts. The first thrust focuses on developing dynamical systems by explicitly modeling day-to-day travel choices of intelligent traveling agents and examining the convergence and stability properties of the dynamical systems. By demonstrating Wardropian user equilibrium can still emerge in the day-to-day evolution of network traffic dynamics with intelligent traveling agents, we hope to provide certain justification for the premise of equilibrium or establish some behavioral basis for the equilibrium modeling paradigm. The second thrust aims to integrate implicit deep learning with network equilibrium analysis to develop an end-to-end framework that directly learns the behaviors of traveling agent, the equilibrium state, and other modeling components, if needed, from empirical data. This end-to-end framework can not only support a much richer representation of travel behaviors but also describe the outcome of strategic interactions among intelligent traveling agents. Once trained, it can be used to conduct “what-if” analyses. In addition, the same modeling framework can be further tweaked to prescribes optimal plans for improving the network performance.

“FISH’N’CHIPS”:  Forecasting and Identifying Supply-and-demand Hindrances aNd Correlated Hazards In Procurement Systems 

Stealth Software Technologies, Inc.; PI- Peter Adriaens

Stealth Software Technologies, Inc. (Stealth) proposes a new supply chain management system for DARPA RSDN TA2. At a high level, our proposed effort involves three components. The first component is (a) applying cutting-edge graph, network, and learning algorithms to SDN data using cutting edge ideas from graph clustering, streaming and multicommodity flow algorithms from theoretical computer science. Next, we have a novel approach to (b) data and function connectors with APIs that are expressive and flexible. These can interface with TA1 systems in a robust way that will produce novel graph analyses and utilize secure multiparty computation and/or differential privacy, and can also be connected to TA3 solutions that heavily rely on automated stress testing. The final component is (c) a revolutionary visualization engine tied into the APIs, enabling easy-to-understand, reactive depictions of the complex underlying analysis. We further elaborate on these tasks in the Technical Plan section.

Regional Coordination to Optimize Wastewater and Stormwater System Operations

Fred A. & Barbara M. Erb Family Foundation; PI- Glen Daigger

The primary goals of the project are to identify barriers to regional stormwater cooperation and then to work towards solutions to these barriers so that the region can better manage the impacts of larger storm events and variability of precipitation driven primarily by climate change. The benefit for the region could be hundreds of millions of dollars in capital savings. This project will focus on the GLWA wastewater service area in SE Michigan which includes portions of Wayne, Oakland and Macomb counties. There are a number of technologies and concepts that currently exist to more effectively manage stormwater flows from a regional perspective. In addition, new approaches can be developed based on new operating parameters which could benefit the region significantly. These approaches have the potential to save hundreds of millions of dollars of capital investment and operations costs.

BRITE Relaunch: A Physics-Based Simulation Model for Exploring Community Resilience to Wildfires

National Science Foundation.; PI- Ann Jeffers

Wildfires pose a substantial threat to civilians living at the wildland-urban interface (WUI). The damage to most structures is caused by ignition from firebrands, which are fragments of flaming and smoldering materials that are carried by the wind and may impinge on structures. While a single firebrand is enough to ignite a home, a greater danger is faced when accumulations of firebrands gather at certain locations on the structure (e.g., in corners, cracks, and crevices). At present, the only way to study how firebrands accumulate around structures is to perform an experimental test. However, such tests are expensive and do not necessarily scale to full-sized structures. Furthermore, the lack of a model for firebrand transport and ignition prohibits advances that seek to understand how policy and data science can inform decision-making for communities at the WUI. This work proposes the formulation and validation of a physics-based simulation model for firebrand transport around structures. The simulation will use computational fluid dynamics (CFD) to simulate the flow of air around a structure due to wind using a multiphase Eulerian approach, and firebrands coming in contact with the structure will be modeled using the Discrete Element Method (DEM). Due to the high level of uncertainty, a stochastic model is proposed that takes into account the variability in firebrand properties, which have been measured from experiments and are published in the literature. Once validated, the simulation model will be used to study how policy decisions affect community resiliency to wildfires.

Impact of UV treatment on microbial communities in a full-scale drinking water distribution system

City of Ann Arbor; PI- Lutgarde Raskin

The proposed research will investigate the effect of UV treatment within a full-scale water treatment plant (AA-WTP) on microbial communities in the distribution system, with a focus on opportunistic pathogens and nitrifying bacteria. Experiments will be conducted to characterize microbial communities within A) a full-scale water treatment plant and distribution system 1) while the UV system is off/operated only intermittently and 2) during a one-year period of continuous operation. Samples will be collected before and after theUV system to characterize the impact of UV on the overall microbial community and target populations. Additionally, sampling will be conducted in the distribution system to determine how continuous UV treatment changes distribution system water quality. The results of this research will provide guidance to the utility regarding the potential benefits and costs of continuous operation of the UV disinfection system, including how the system may influence microbial communities, microorganisms of concern, and general water quality.

Leveraging Vehicle Telemetry Data to Reduce Congestion in Urban Traffic Networks

General Motors Company; PI- Yafeng Yin

Utilizing GM’s high speed vehicle telemetry (HSVT) data, the University of Michigan (UM) team, in collaboration with General Motors LLC Global R&D, aims to develop novel computational techniques that prescribe strategies to better manage and operate existing traffic networks to reduce traffic congestion. This project contributes to GM’s efforts of achieving the “zero congestion” for future transportation. It leverages recent advancements in machine learning to develop an end-to-end modeling framework that would transform the way how metropolitan planning organizations model, plan and manage their traffic networks.

Multidisciplinary Investigation of Transmission to Ease influenza (MITIGATE FLU)

National Philanthropic Trust; PI- Krista Wigginton

The overall goals of this project are to innovate influenza virus detection and quantification and to probe mechanisms of virus inactivation in air and on surfaces. These goals will be achieved by systematically and simultaneously optimizing both sampling and detection. The final products of this effort will be accurate and rapid platforms for influenza virus detection within indoor air and on fomites, as well as mechanistic descriptions of influenza virus fate in the indoor environment. Our cross-disciplinary team of experts in environmental virus surveillance, influenza virology, protein mass spectrometry, pathogen sensors, and aerosol science is uniquely positioned to address this persistent issue.

Collaborative Research: NNA Research: Interactions of natural and social systemAnalysis and Characterizing Sludging in Flat Sheet Membranes

Fibracast; PI- Glen Daigger

The objectives of the research to be conducted in accordance with the scope of work are as follows:
1.To characterize the fluid flow characteristics of Fibracast commercial membrane cassettes tobetter understand how to optimize membrane system performance.
2.To verify computational fluid dynamic (CFD) model experimentally.
3.To investigate and verify potential improvements to commercial membrane cassette.
4.To investigate potential additional applications for Fibracast membrane products.
This project emphasizes two types of stressors: (a) changes in summer and winter heating regimes that have led to growth of tall vegetation in the tundra, and an increased frequency of winter warm spells that change snowpack structure and seasonality and (b) growing presence of industrial activities/infrastructure as well as increasing social complexity of reindeer herding economy. Building on state-of-the-science understandings of impact paths of these stressors, we propose a set of hypotheses to be tested via a transdisciplinary approach, and synthesis of traditional and scientific knowledge. The project addresses all six focus areas of the NNA solicitation. Based on Track 2 team development activities, our group (22 scientists) has developed a comprehensive management and integration plan for effective coordination and integration of disciplinary research tracks.

FW-HTF-R: Collaborative Research: Partnering Workers with Interactive Robot Assistants to Usher Transformation in Future Construction Work

National Science Foundation; PI- Seymour Spence

Extreme windstorms cause massive economic loss and societal disruption each year. According to the National Oceanic and Atmospheric Administration, in 2020 the United States was subject to 22 weather events that caused more than one billion dollars of losses, with Hurricane Laura the costliest causing 19 billion dollars of losses and leaving more than a million people without power. To increase the resilience of the built environment against extreme winds there is huge interest from the civil engineering
community to radically change how buildings, a key part of the built environment, are designed to resist extreme winds through the application of methodologies based on the concepts of performance-based engineering. This new paradigm, referred to as performance-based wind design (PBWD), centers on the explicit evaluation of performance at all hazard intensities, including at ultimate load levels where significant damage is expected, while systematically treating uncertainty through reliability. This industry-led drive for PBWD has resulted in the recent publication of a first-of-its-kind Pre-standard on PBWD by the American Society of Civil Engineers and created a fundamental need for technologies that can assist practicing engineers in carrying out PBWD of structural systems through advanced reliability-based analysis procedures. The technology of this project fills this critical gap through providing an initial software tool for reliability-based PBWD that leverages the basic research breakthroughs in probabilistic dynamic shakedown of wind excited structural systems that were spawned by the NSF lineage award: CMMI-1462084: Collaborative Research: Performance-Based Framework for Wind-Excited Multi-Story Buildings. The target market for the technology are the thousands of civil and structural engineers tasked with designing structural systems that possess greater resilience to extreme wind events, while meeting the stakeholder’s need for cost effectiveness. It is estimated that there are over 300,000 civil engineers in the United States, with over 50,000 specifically working in the space of structural engineering and therefore the design of systems to resist the actions of natural hazards.  

MS2 enumeration in samples

MagPlasma, Inc.; PI- Alexsandra Szczuka

The purpose of these trials is to inform system optimization and to determine disinfection e

Integrated biochemical and electrochemical technologies (IBET) to convert organic wastes to biopower via North American research and educational partnerships

Department of Energy; PI- Lutgarde Raskin

We propose to develop an integrated biochemical and electrochemical technologies (IBET) system to convert mixed urban and suburban organic waste streams to pipeline-ready biomethane. The IBET system consists of three synergistic components. A first-stage anaerobic dynamic membrane bioreactor (AnDMBR), modeled after the stomach of ruminants, will be used to enhance hydrolysis and achieve rapid short chain carboxylic acids (SCCAs) generation (expected organic loading rate is 24 g volatile solids (VS)/L reactor/day at a 12-h hydraulic retention time (HRT)). A second novel AnDMBR with enhanced biofilm surface area and recirculation (MagnaTree bioreactor) will convert the SCCAs produced in the first-stage AnDMBR to biogas with an expected yield of 0.4 L CH4/g VSfed at a 5-day HRT. In the final stage, an electrochemical separation system, called the Electrochemical Reactor for CO2 and H2 Delivery (ERCHD), electrolyzes water into H2 and captures biogas CO2 as aqueous HCO3-. The produced H2 and captured HCO3- serve as substrates for a second MagnaTree bioreactor focused on hydrogenotrophic methanogenesis, which produces an extra ~38% of high-purity (98%) CH4. The project aims to evaluate the performance of this IBET system at the pilot-scale level at the Great Lakes Water Authority water resource recovery facility (WRRF), perform a techno-economic analysis and a life-cycle assessment, and develop industry recommendation. We anticipate greater than 25% more biomethane production and 25% less operating cost compared to the State of Technology. We propose to integrate our research with innovative educational efforts to advance both waste-to-energy (W2E) technology and next-generation workforce development. We will create an educational consortium to connect the leading institutions and collaborating partners. Collaborations among five North American universities will expand our research potential and promote an understanding of the North American W2E context. Involving a WRRF, three engineering companies, and a national lab partner will be especially effective taking into account real-world complexities from the start. These partnerships also allow the sharing of educational resources and create meaningful training opportunities for diverse students at different levels. The consortium will achieve its goals through three major activities: Ph.D. student research rotations, co-op-based master student training, and research experiences for undergraduates. We will recruit students from partner and collaborating universities and community colleges. Diverse students will be recruited through our existing networks with Minority Serving Institutions.

Collaborative Research: Cascade “Ecohydronomics” in the Amazonian Headwater System

National Science Foundation; PI- Valeriy Ivanov

Tropical forests yield a third of global evapotranspiration from land surface. Nearly half of the water that falls on the Amazon rainforest, the world’s largest contiguous tropical forest, passes through stem xylem and leaf stomata on its way back to the atmosphere. There has been progress in quantifying large-scale water fluxes in Amazonia, however, ecophysiological and soil-water processes controlling landscape variability in transpiration, and the unilateral, cascading impact of forests residing in groundwater recharge (GW-R) areas on hydrology and tree function in discharge (GW-D) zones remains poorly understood. We hypothesize that (H1) there is a strong landscape variation in forest
transpiration capacity due to distinct ecophysiological traits of trees residing in GW-R and GW-D regions; (H2) previously unquantified “hybrid” hydraulic behavior of tropical soils governs flux quantity and the transit time scales of soil water transport connecting GW-R and GW-D areas; and (H3) the composition and ecophysiology of forests in topographic highs control the one-way cascade GW-R→GW-D connection and shape its sensitivity to climate variations, determining the distribution of ecophysiological traits and
transpiration in topographic lows. The specific objectives are (O1) to estimate forest transpiration capacities in GW-R and GW-D areas based on physiological trait measurements and mechanistic modeling for two locations in the Amazon of distinct climate seasonality; (O2) to inversely estimate properties of the hybrid soil hydraulic system based on deep soil water observations using novel tools in
probabilistic learning and models of soil hydraulics; (O3) to infer effective soil hydraulic properties governing GW-R→GW-D connection in a case study tropical watershed in central Amazonia; (O4) Integrate ecophysiological and hydrological models to estimate the spatial distributions of GW-R→GW-D water transit times; and (O5) to quantify watershed-scale GW-RGW-D flux sensitivities to climatic perturbations at seasonal and interannual scales, including long-term change of vegetation traits.

Next-generation performance-based wind engineering: knowledge and computational modeling advances for collapse characterization

National Science Foundation; PI- Seymour M.J. Spence

Each year extreme windstorms cause massive economic and societal loss around the world. Estimates put the losses to the United States to hurricanes alone, over the past five years, to be in excess of $300 billion. This has led to intense interest from both wind engineering researchers and practitioners to transition from traditional prescriptive approaches in favor of performance-based engineering (PBE) methodologies. A direct consequence of these efforts has been the development of state-of-the-art frameworks and guidelines for the application of what has become known as performance-based wind engineering (PBWE). Despite these efforts, a fundamental lack of knowledge still exists on the behavior at collapse of integrated structural and envelope systems of typical wind excited buildings that adhere to current codes and standards. This lack of knowledge is coupled with an absence of efficient and practical computational modeling approaches that are capable of not only assessing such an integrated collapse scenario, but also accounting for the inevitable uncertainty that exists in estimating collapse. These essential gaps in knowledge and modeling severely limit the benefits of current PBWE methodologies. The main goal of this project is to provide the necessary advances in knowledge and modeling to overcome these fundamental hurdles. This will be achieved through the creation of a comprehensive knowledge base concerning the probability of collapse of typical structural systems subject to extreme winds and how this correlates to the performance of typical building envelope solutions. This knowledge base will enable the introduction of a new generation of probabilistic computational modeling frameworks for the rapid and integrated structural and envelope collapse assessment. These advances will arm engineers with the necessary tools for reaping the maximum benefits offered by PBWE during the integrated design of building systems against extreme winds. This will lead to transformative innovation in design, while providing the fundamental knowledge advances necessary for the development of next generation PBWE guidelines and standards.

Collaborative Research: NNA Research: Interactions of natural and social systems with climate change, globalization, and infrastructure development in Yamal (Russian Arctic)

National Science Foundation; PI- Valeriy Ivanov

The Arctic warming has triggered seasonality shifts, permafrost warming, and the occurrence of extreme weather conditions. Terrestrial ecosystems have already responded with phenology changes, lower-latitude plants moving into the tundra, and cascading northward displacement of mammalian and bird populations. These changes have put increased pressure on livelihoods of peoples of the North, who depend on flora and fauna tied inextricably to weather and seasonality. The Arctic is also being impacted by globalization: the emergence of industrial and urban “hot spots” and centers of hydrocarbon extraction, the development of tourism, and the growing influ-ence of “newcomers” are impacting the tundra and livelihoods of indigenous peoples as
never before. Multi-system responses of Arctic systems to both climate change and globalization is urgently needed.
Following our Track 2 project team integration and synthesis activities, we propose to study natural, social, and built environment systems of the Yamal region of Russia – a tightly bound, circum-scribed “microcosm” of the Arctic. The region spans four of the five Arctic bioclimatic subzones; it bears signatures of the tightly woven processes linking climate, weather, landscapes with permafrost, plants, animals and the increasingly interacting worlds of the Nenets (the indigenous pastoralists subsistent on reindeer) and global economy bringing to the region reindeer product businesses and the hydrocarbon industry. Understanding how these abiotic, biotic, social, and built environment systems are linked in their responses to stressors of the “new Arctic” is our primary objective.
This project emphasizes two types of stressors: (a) changes in summer and winter heating regimes that have led to growth of tall vegetation in the tundra, and an increased frequency of winter warm spells that change snowpack structure and seasonality and (b) growing presence of industrial activities/infrastructure as well as increasing social complexity of reindeer herding economy. Building on state-of-the-science understandings of impact paths of these stressors, we propose a set of hypotheses to be tested via a transdisciplinary approach, and synthesis of traditional and scientific knowledge. The project addresses all six focus areas of the NNA solicitation. Based on Track 2 team development activities, our group (22 scientists) has developed a comprehensive management and integration plan for effective coordination and integration of
disciplinary research tracks.

FW-HTF-R: Collaborative Research: Partnering Workers with Interactive Robot Assistants to Usher Transformation in Future Construction Work

National Science Foundation; PI- Carol Menassa

The construction industry is ill-famed for its stagnant productivity, use of antiquated work processes, adversarial relationships among stakeholders, and safety and health issues among construction workers. Over time, this has led to a chronic shortage of skilled workers, largely due to an aging and retiring workforce and the reluctance of younger generations or people of different abilities to pursue such careers. This FW-HTF project envisions that intelligent human-robot teams have the potential to transform future construction work and the profile of future construction workers resulting in new career
opportunities and significant benefits to the industry. In this research, human workers will use interactive task learning technology to teach co-robots to perform construction work tasks resulting in symbiotic human-robot teams that can be widely deployed in the construction industry. The future human construction worker will be responsible for high-level work planning and will transition to the role of teacher and supervisor of the co-robot. The co-robot, on the other hand, will perform physically strenuous work tasks and collaborate with the human supervisor to improvise when unforeseen work conditions are
encountered. This envisioned approach parallels the classical Master-Apprentice vocational model prevalent in today’s construction industry, wherein novice human workers develop skills by completing apprenticeships under the tutelage of skilled workers and accumulating experience in diverse work conditions. This transition will be achieved through innovation in the construction vocational curriculum, where, in addition to learning fundamentals of construction work, human workers will learn and use new interaction, visualization and trust-development methods to collaborate with co-robots. These new skills will enable workers to advance their work roles, encourage upward mobility, and open avenues for people of diverse physical abilities to be productive members of the construction workforce, a prospect that is impossible today. This will result in improved productivity and reduced costs associated with re-work and safety in construction work. A tight knit partnership with industry collaborators will inform the research activities and provide access to interactions with a broad range of construction workers.

AI-Based EHS Management

VelocityEHS; PI- SangHyun Lee

Recent advancements in Artificial Intelligence (AI) techniques have enabled Environment,
Health, and Safety (EHS) practices to be smarter and more automated. We aim to define the next generation of EHS management by fully utilizing the potential of AI through a 5-year project with multiple objectives (described below) that can be directly translated into todays’ EHS practices. This project requires significant field trips to collect the data and validate our
developed techniques.