Projects and Grants

Below, is a list of active funded projects and grants led by the COSC faculty. For more information, please contact individual faculty members.


FW-HTF-RM: Augmenting Spatial Cognition Capabilities of Future Workforce to Enhance Work Performance in Altered Environments Using Virtual Reality

U.S. National Science Foundation (NSF)

October 2019 to September 2022

PI: Dr. Manish Dixit

This research project will enable the future workforce to work in unfamiliar environments, including desolate hard to reach places such as deep space, low Earth orbit, deep ocean, and polar regions. Virtual Reality (VR), eye tracking, and electroencephalography (EEG) will be combined in a cost-effective educational platform to inform design principles for scenario-based simulations and games to train the future workforce to adapt to and work in altered environments.


Guidance for the Use of UAS During Suboptimal Environmental Conditions

Texas Department of Transportation (TxDOT)

October 2019 to September 2021

Co-PI: Dr. Youngjib Ham

This research project will address key challenges of Unmanned Aerial System (UAS) operations for visual monitoring in suboptimal conditions (e.g., wind, rain, mist, smoke, and ambient lighting). The outcome of this study can provide guidance for UAS flight operations in suboptimal conditions, and recommend settings, procedures and workflows to ensure data quality collected by UAS for highway mapping, bridge inspection, crash site data collection, and real-time traffic monitoring.


FW-HTF-P: Collaborative Research: Anthropocentric Robot Collaboration in Construction

U.S. National Science Foundation (NSF)

September 2019 to August 2020

PI: Dr. Ryan Ahn

This research project will carry out a human-centered investigation where a human worker's response to different scenarios of human-robot collaboration in construction is non-invasively and continuously monitored in order to maximize the overall performance of human-robot collaboration. The outcome of this study has the potential to build foundational knowledge on how we can prepare our existing and new workforce for future construction.


ERC Planning Grant: Engineering Research Center for AI Construction (AI-Con)

U.S. National Science Foundation (NSF)

September 2019 to August 2020

Co-PI: Dr. Amir Behzadan

This research project will support the development of a research roadmap for implementing artificial intelligence (AI) in the construction industry and the formation of a multi-institutional team working toward an NSF Engineering Research Center (ERC). Impacts of this ERC will include significant advancements in AI algorithms, human-machine interfacing, machine learning for generative design, and deep learning that will transform how construction projects operate from conception to design to completion.


International Resarch Project: Digitalization of Construction Contract requirements using Artificial Intelligence and Natural Language Processing

Institute of Information and Communications Technology Planning and Evaluation (IITP, Korea)

July 2019 to December 2020

PI: Dr. David Jeong

This research project is an international collaboration with a group of researchers at Yonsei University in South Korea. The goal is to explore and test AI and NLP-based algorithms that can analyze major construction contract requirements from a large number of historical construction contract documents and then, automaticcaly extract and organize major contractual requirements of a new project with a possible level of risk identification.


Anaylsis of the Delivery of DB Compared with DBB Projects

Maricopa Association of Governments and Arizona Department of Transportation (ADOT)

May 2019 to March 2020

Co-PI: Dr. David Jeong, Dr. KC Choi

This research project will analyze, compare, and contrast three design-build (DB) projects. A design-bid-build (DBB) project will be used as a control project which is similar to the three DB projects in terms of key project parameters such as scope, function, and type. This project will identify similarities and differences between the contractual methods via highly extensive case studies. The primary focus of the study will be to gain better understanding of the cost and time differences between the two contractual methodologies and other lessons learned.


Developing an Improved South Dakota Construction Cost Index

South Dakota Department of Transportation

April 2019 to March 2020

PI: Dr. David Jeong

This research project will identify current and potential uses for construction cist indices (CCIs) in South Dakota Department of Transportation and develop methodologies for calculating, maintaining, and using a CCI for each use.  This study will also evaluate the level of risk or uncertainty for projections made using the CCI methods.


Systematic Approach for Estimating Construction Contract Time: A Guidebook (NCHRP 08-114)

National Cooperative Highway Research Program (NCHRP), Transportation Research Board (TRB), National Academy of Science

August 2018 to April 2020

PI: Dr. David Jeong, Co-PI: Dr. KC Choi

This research aims at developing a comprehensive guidebook encompassing procedures, methods, and tools for determining contract time that can work for a wide spectrum of highway infrastructure projects.  A systematic approach and a risk-based methodology will be employed to provide reliable contract time estimation methods over the project delivery process.


Cognition-Driven Display for Navigation Activities (Cog-DNA): Personalized Spatial Information System Based on Information Personality of Firefighters

National Institute of Standards and Technology (NIST)

June 2018 to May 2021

Co-PI: Dr. Patrick Suermann

This project proposes and tests an innovative concept called Spatial Information Personality (SIP), a cognitive profile of information-taking preference and behavioral patterns at the individual level. SIP of firefighters will be tracked by quantifying individual reactions to different types, quantities, and display methods of information during virtual reality based fire training. The collected SIP data of individual firefighters can be integrated as a necessary part of their personal files.


Collaborative Research: Transforming Teaching of Structural Analysis through Mobile Augmented Reality

U.S. National Science Foundation (NSF)

August 2017 to July 2020

PI: Dr. Amir Behzadan

The objective of this collaborative project is to transform existing teaching pedagogy in structural analysis by designing and testing a mobile augmented reality (AR) platform that superimposes the visuals of the textbook with interactive computer generated 3D models of structures under load. In doing so, the potential of AR for improving learning and increasing student engagement in the learning process is systematically assessed.


Non-intrusive Elderly Smart-home Healthcare System for Monitoring Short-term and Long-term Anomaly in Daily Activity Patterns

Korea Agency for Infrastructure Technology Advancement (KAIA)

March 2017 to December 2019

PI: Dr. Ryan Ahn

this research designs a smart home monitoring platform of elderly people’s daily activities to improve their health. The proposed platform will continuously monitor and evaluate daily activities of the elderly, and identify the occurrence of emergent situations (including accidents) and the decline in physical and cognitive functions.


Uncovering Potential Risks of Wind-induced Cascading Damages to Construction Projects and Neighboring Communities

U.S. National Science Foundation (NSF)

January 2017 to December 2019

PI: Dr. Youngjib Ham

This project will create and validate a new streamlined Imaging-to-Simulation framework to prevent wind hazard events from causing catastrophic damage to construction projects and neighboring communities.


A Natural Language Based Data Retrieval Engine for Automated Digital Data Extraction for Civil Infrastructure Projects

U.S. National Science Foundation (NSF)

September 2016 to December 2019

PI: Dr. David Jeong

This research proposes a novel approach for a fast and unambiguous reuse of digital models for the civil infrastructure industry by developing an automated data retrieval engine capable of recognizing user information from their natural language queries (e.g., words, phrases, questions) and extracting the desired data from heterogeneous digital datasets by employing the recent advances in Natural Language Processing (NLP) techniques, machine-learning based semantic measure methods to develop the data retrieval system.


Revealing Hidden Safety Hazards using Workers' Collective Bodily and Behavioral Response Patterns

U.S. National Science Foundation (NSF)

January 2016 to December 2019

PI: Dr. Ryan Ahn

The objective of this research is to examine whether, how, and to what extent workers' collective bodily and behavioral response patterns identify recognized/unrecognized hazards for the purpose of enhancing safety performance in construction environments.

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