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Texas A&M University College of Architecture

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.


A Hybrid Decision Support System for Driving Resiliency in Texas Coastal Communities

Texas Sea Grant (National Oceanic and Atmospheric Administration)

February 2020 to January 2022

PI: Dr. Amir Behzadan

This research project augments flood management practices in Texas coastal communities through citizen science, artificial intelligence (AI), decision science, and cyberinfrastructure. The team will develop a mobile app that takes crowdsourced data, calculates flood risk, and estimates floodwater depth at the street-level. Generated data are then incorporated in a CyberGIS spatial decision support system (SDSS) to assist in decision-making.


RT383: Quantitive Validation and Deployment of Industrial Integrated Project Delivery (I2PD)

Construction Industry Institute (CII)

January 2020 to December 2021

PI: Dr. David Jeong

This research project will investigate whether there is a measurable increase in project success rates using I2PD and other related integrated delivery methods as compared to traditional delivery methods. Findings will confirm whether the findings of CII RT341 are consistent with the actual results obtained from live projects that apply some or all of the principles and methods of I2PD.


Guidance for the Use of UAS During Suboptimal Environmental Conditions

Texas Department of Transportation (TxDOT)

November 2019 to October 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.


INVEST Training Module Development for Use by Universities

US Federal Highway Administration (through ICF International, Inc.)

November 2019 to July 2021

Co-PI: Dr. Phil Lewis

The objective of this project is to develop materials and resources to inform and support current and future users of the FHWA's INVEST tool.  The training materials will be developed as a stand-alone semester-long course and will include training modules to provide more opportunities for the INVEST curriculum to be incorporated into multiple course disciplines.  Marketing materials will be developed to support outreach to universities and colleges.


Real World Data Measurement of Factors Affecting Air Quality for Nonroad Diesel

U.S. Department of Transportation (through the Center for Advancing Research in Transportation Emissions, Energy, and Health)

November 2019 to May 2021

PI: Dr. Phil Lewis

This research project addresses a gap in knowledge related to the understanding of which contribute significantly to air quality for nonroad equipment operators.  The team will identify, measure, and characterize how the equipment, operator, and environment work together to create air quality conditions for equipment operators.


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.


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 in 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 Research 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, automatically extract and organize major contractual requirements of a new project with a possible level of risk identification.


Nonroad Equipment Activity and Data Analysis

U.S. Environmental Protection Agency (through Eastern Research Group, Inc.)

July 2019 to June 2020

PI: Dr. Phil Lewis

The objective of this project is to collect and analyze real-world activity data from a variety of off-road construction vehicles and equipment in order to update and improve the U.S. Environmental Protection Agency's (EPA) NONROAD model, which is used for estimating fuel use and emissions.  Public and private sector fleet owners may realize many benefits of an updated and improved NONROAD model based on real-world data.


Phase II - Construction Activity Sequencing Logics using Daily Work Reports Data

Montana Department of Transportation

May 2019 to May 2020

PI: Dr. David Jeong

This research project will develop construction activity sequencing logics for different types of highway projects based on historical data, which can help quickly identify the most common work sequence of the given project and determine the project schedule. Results are expected to significantly improve the accuracy and reliability of project scheduling practices.


Analysis 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.


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

U.S. National Science Foundation (NSF)

January 2017 to December 2020

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 February 2020

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 2020

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.