Workshop Time:              8:00 am to 4:00 pm, August 12th, 2024

                                             3121 West Gate City Boulevard, Greensboro, North Carolina 27407

Workshop Committee:        Dr. Yuhan Jiang,, Dr. Benjamin Uwakweh, and Dr. Musibau Shofoluwe

   Department of Built Environment, North Carolina A&T State University 

Tentative Agenda:

Sunday, August 11th, 5:00 pm to 7:00 pm, Welcome Reception


Monday, August 12th, 8:00 am to 4:00 pm, Workshop Day, including Buffet Breakfast, Keynote Presentation/Panel Discussion I, Morning Coffee break, Group Brainstorm Discussion I, Buffet Lunch, and Keynote Presentation/Panel Discussion II, Afternoon Coffee Break, Group Discussion II, and more.

Keynote Speakers and Presentation Information:

Dr. Jiansong Zhang, Associate Professor

Founder/Director of Purdue Automation and Intelligent Construction (AutoIC) Lab

Purdue University

“Father of Invariant Signatures in AEC” 

Dr. Jiansong Zhang is an Associate Professor of Construction Management Technology at Purdue University. He is the “Father of Invariant Signatures in AEC”. Dr. Zhang earned his Bachelor of Construction Management from Huazhong University of Science and Technology in Wuhan, China with the top grade in his department, his M.Sc. in Civil and Environmental Engineering from Carnegie Mellon University, and Ph.D. in Civil Engineering from the University of Illinois at Urbana-Champaign. He worked in the Civil and Construction Engineering Department at Western Michigan University as an Assistant Professor for two years before joining the School of Construction Management Technology at Purdue University as an Assistant Professor in Aug. 2017. Dr. Zhang’s research resulted in 100+ refereed journal and conference publications, multiple U.S. patents (some pending or provisional), 10+ technology disclosures, multiple published datasets and technical reports, and tens of conference talks. Dr. Zhang’s technologies have been adopted by industry such as domestic software company and international organizations. Dr. Zhang has chaired the Ph.D. committee of 11 Ph.D. students and advised 3 Postdoctoral Researchers and 7 additional Ph.D. students, in Thesis Committees of 8 Masters Students, and served as 17 undergraduate students’ research mentor. Dr. Zhang always values diversity and inclusion, the Ph.D. students and PostDocs he advised are from four different continents and 50%+ of them are female or minorities. Dr. Zhang has won multiple awards such as the prestigious Purdue Corps of Engagement Award in 2022, the prestigious The John P. Lisack Early-Career Engagement Award in 2022, and was awarded SCMT Outstanding Faculty in Engagement in 2022. Dr. Zhang has also been awarded the inaugural “Purdue Polytechnic Institute Interdisciplinary Research Collaboration Award” as one of two only recipients in the whole college. Furthermore, Dr. Zhang’s productive technology disclosures to the Purdue Research Foundation led to him celebrated as one of sixteen Purdue University inventors during the National Inventors Day 2022. Dr. Zhang is recognized as an National Science Foundation (NSF) Division of Civil, Mechanical and Manufacturing Innovation (CMMI) Panel Fellow and has served as an NSF panelist many times for proposal reviews at different programs such as CMMI, IIP, FW-HTF, and DUE. Dr. Zhang is currently serving as the Chair of ASCE Data Sensing and Analysis (DSA) Committee and Technical Vice-Chair of ISARC 2024. 

Presentation: Invariant Signatures for BIM Interoperability

Abstract: Building Information Modeling integrates 3D models with physical and functional characteristics of a building and infrastructure project and has the potential to facilitate the exchange of information between different parties involved in the same project throughout its lifecycle, ranging from design and construction to maintenance and operation, and beyond. However, the fundamental problem -- lack of interoperability (ie. the inability to exchange information between different platforms) due to model inconsistency and missing information -- prevents the exchange of such information. Existing research efforts to address this lack of interoperability have been heavily focused on standardization and semantic modeling. These standard methods do not address the underlying problem and still depend on the computer models involved. In this talk, I will introduce the concept of invariant signatures of architecture, engineering, and construction (AEC) objects, its discovery and application in solving BIM interoperability problem in a radically different approach from the existing efforts that are focused on data schema standardization and/or term-based semantics of AEC objects. Invariant signatures of an AEC object are defined as a set of intrinsic properties (e.g., geometry, location, material) of the object that distinguish it from other objects, and they do not change with software implementation, modeling decisions, and/or language and cultural contexts. An interdisciplinary approach involving geometry theorems, computer algorithms, and material mechanics was employed to explore and quantify these intrinsic properties. The underlying hypothesis is that invariant signatures of an AEC object collectively defined by the Cartesian points-based geometric, relative location and orientation, and material mechanical properties will enable seamless and universal interoperability of building information modeling (BIM) software in various analysis phases from architectural design and preliminary structural design to detailed structural analysis and construction cost estimation. Successes in applying invariant signatures to different BIM interoperability scenarios will be introduced.

Dr. Pingbo Tang, P.E., Associate Professor

Department of Civil and Environmental Engineering

Carnegie Mellon University

NSF CAREER Award Winner 2015

Prof. Pingbo Tang is an expert on civil infrastructure operations and human systems engineering for civil infrastructure operational safety. Prof. Tang’s research explores remote sensing, human systems engineering, and information modeling technology in support of the spatiotemporal analyses needed for effective management of production systems, construction sites, constructed facilities, and civil infrastructure systems. His ongoing studies have examined sensing and modeling methods for comprehending the Human-Cyber-Physical-Systems (H-CPS) in accelerated construction and infrastructure operations (e.g., airport operations, nuclear plant outage control). He won best paper awards at multiple conferences, the 2013 Recent Alumnus Achievement Award of the Civil and Environmental Engineering Department at Carnegie Mellon University, the National Science Foundation CAREER Award in 2015, and the Daniel W. Halpin Award for Scholarship in Construction from the American Society of Civil Engineers in 2020. 

Presentation: Reimagining Civil & Environmental Engineering Education

Abstract: This presentation overviews the transformation of civil and environmental engineering education and relevant research efforts at Carnegie Mellon University. Decades of computing research in civil and environmental engineers show the importance of leveraging computational tools in supporting civil and environmental engineers in data-driven simulation, learning, and decision-making. These computational tools are forming a new vision of digital twins in civil and environmental engineering and the underlying convergence of human understanding, machine learning, and Artificial Intelligence in enabling the resilience of civil systems in climate changes. Undergraduate and graduate education and research are under systematic updates at Carnegie Mellon to enable a unified learning and decision-making framework to support learning experiences while fostering a data culture for future civil and environmental engineering projects. The presentation will highlight the threads of sensing and computing in the curriculum at both undergraduate levels, emphasizing the emerging online certificate of “AI Engineering - Digital Twins and Data Analytics.”

Dr. Jizhong Xiao, SM, IEEE, Professor

Director of CCNY Robotics Lab

The City College of New York

Co-Founder of InnovBot LLC

NSF CAREER Award Winner 2007

Dr. Jizhong Xiao received Ph.D. degree in Electrical and Computer Engineering from the Michigan State University in 2002. He is currently Professor of Electrical Engineering at the City College of New York (CCNY/CUNY City College) and Professor of Computer Science at CUNY Graduate Center. He is the Director of CCNY Robotics Lab and Co-Founder of InnovBot LLC, a CUNY Spin-off company dedicated to R&D and commercialization of robotic and NDE technologies with application to the inspection of infrastructure and wind turbine blades. He has published more than 200 research articles in peer reviewed journals and conferences. He received NSF CAREER Award in 2007 and CCNY Outstanding Mentoring Award in 2011, Humboldt Research Fellowship in 2013~2015, and Dean’s Award for Excellence in 2019. He obtained research grants from NSF, DOT and FHWA to develop technologies and software related to robotics and NDT. He is the inventor of City-Climber, Rise-Rover, GPR-Rover and Wind-Rider robots. 

Presentation: Robotic Inspection of Infrastructure Using vision, GPR, and Impact-Echo Sensors

Abstract: Numerous infrastructure (bridges, dams, highways, tunnels, etc.) in USA and around the world are reaching their life expectancy, and thus have strong needs for routine inspection and maintenance to ensure sustainability. It is reported that 42% of over 600,000 highway bridges in the National Bridge Inventory (NBI) have exceeded their design life of 50 years, and 42,951 bridges are rated in poor condition and classified as “structurally deficient”. Although normal bridges are inspected biennially (deficient bridges are inspected annually), study has found that the visual inspection results can have a high level of inconsistence in identifying surface flaws. To inspect the structural integrity of bridges, the inspectors also need to detect subsurface defects (i.e., delamination, voids) using NDE instruments such as GPR and impact-echo (IE) device at difficult to access components (i.e., pier, bottom side of the deck). The current practice of manual inspection using hand-held NDE devices by a “spider-man” with rope access, or by using scaffolding or snooper truck has to block traffic, and is expensive, time-consuming, and exposes human inspectors to dangerous situations. This presentation will introduce climbing robots developed over the years at CCNY Robotics Lab that integrate the robot control and vision-based accurate positioning with NDE signal processing to detect both surface flaws and subsurface defects. These robots combine the advantages of aerodynamic attraction and suction to achieve a desirable balance of strong adhesion and high mobility. For example, Rise-Rover with two drive modules can carry heavy payload up to 20 Kg, and GPR-Rover can carry a small GPR antenna for subsurface flaw detection and utility survey on concrete structures. Empowered by the capability of low cost vision-based accurate positioning, the GPR-Rover is able to scan the surface in arbitrary trajectories and tag GPR samples with position information that enables high-resolution 3D GPR imaging. The use of the robotic inspection tool will eliminate the time, hassle and cost to layout grid lines on flat terrain, and make it possible to automatically collect NDE data with minimum human intervention. These robots can reach difficult-to-access areas, take close-up pictures, perform contact-based NDE data collection for further analysis. This presentation will also introduce machine learning algorithms for visual inspection to detect and measure cracks; IE data processing methods that utilizes both learning-based and classical methods to interpret the IE data and reveal subsurface objects; and DNN-based GPR data analysis software to reveal subsurface targets for better visualization. The correlation of findings from multiple NDE sensors (visual, IE and GPR) on board the robot gives a comprehensive evaluation of concrete structures. Field tests demonstrate that the robotic inspection system significantly increases the data collection speed, and make the full bridge inspection faster, safer, and cheaper without affecting traffic flow on roadways. 

Panelist Professional VDC-focus (More coming soon):

Cody Whitelock, CM-BIM, Virtual Construction Manager, Barnhill Contracting Company

Jonathan Harris, Senior VDC Manager, Brasfield & Gorrie, LLC

Zak Ball, Senior VDC Manager, Brasfield & Gorrie, LLC

Amol Soman, PMP, Director, Virtual Design and Construction, Samet Corporation

Panelist Research and Tech Innovation-focus (More coming soon):

Dr. Jizhong Xiao, Professor, The City College of New York

Co-Founder of InnovBot LLC (SBIR Phase I & II)

Panelist Technology and Engineering Education-focus (More coming soon):

Clay Gloster, Jr., Ph.D., P.E., Vice Provost for Graduate Research and Dean of the Graduate College, North Carolina A&T State University 

Kaiwen Cheng, CEO/ President, Queen City Robotics Alliance

Lewis Waller, Ph.D., Chairperson & Associate Professor, Department of Construction Management, Morgan State University

Trina Fletcher, Ph.D., Associate Professor, School of Universal Computing, Construction & Engineering Education, Florida International University

NSF CAREER Award Winner 2022

Charnell Long, Ph.D., Assistant Professor, Department of Educator Preparation, North Carolina A&T State University 

Exhibation VDC Research and Tech Innovation (More coming soon):


Sheraton Greensboro Hotel offers a discounted group rate of $149.00 plus tax for a King or Double/Double guest room. 

We recommend driving/flying to Greensboro (GSO) directly. Free shuttle is available from and to Piedmont Triad International Airport (GSO). Overnight parking is free for workshop participants.  

You may also consider flying to the Raleigh (RDU) Airport (about 69 miles, 1 hr 1 min) or Charlotte (CLT) Airport (about 94.9 miles, 1 hr 22 mins) 

If the total price of a flight with the car rental or car share (Uber/Lyft) is more reasonable. The estimated Uber/Lyft cost is around $75 from RDU and $120 from CLT.

Purpose of the Planning Grant and Workshop

The purpose of this NSF HBCU-EiR planning grant is to develop a broadly accepted strategy and plan (pipeline) to encourage underrepresented minority students to pursue undergraduate and graduate degrees in infrastructure and building construction technology and to prepare them for careers in the nation’s infrastructure and building construction industry. Hence, the following two ideas for increasing student enrollment and learning will be broadly discussed in the diversity group of educational, academic, and industrial experts during the in-person workshop to develop an implementation plan:

· Idea 1: Using introductory hands-on training in emerging technologies (i.e., drone photogrammetry/ mobile LiDAR, building information modeling/digital twin, virtual reality/augmented reality, artificial intelligence, and robotics) to increase student enrollment in infrastructure and building construction technology programs at HBCUs.

· Idea 2: Integrating emerging technologies into the undergraduate curricula in infrastructure and building construction technology programs at HBCUs to equip the underrepresented minority students with employment and research skills in Virtual Design and Construction (VDC).

Expected Workshop Activities and Outcomes

· Communicate with the infrastructure and building construction industry on the current and future needs for employment skills.

The communication will aim to discuss the gaps and challenges in the existing workforce training and education, as well as the expectations and requirements for the HBCU infrastructure and building construction technology program graduates. Specific questions regarding the adoption of emerging technologies in infrastructure and building construction practices will be discussed as well. The communication results will provide comprehensive information to (a) develop the VDC curriculum and course modules for HBCUs, and (b) identify metrics for VDC learning outcome measurement.

· Establish the HBCU Infrastructure and Building Construction Consortium (HBCU-IBCC).

HBCU-IBCC will serve as a network of HBCU infrastructure and building construction technology programs in the United States. It will include institution members of HBCUs like, North Caroline A&T State University, Morgan State University, Alabama A&M University, Florida A&M University, Jackson State University, Tennessee State University, Tuskegee University, Virginia State University, etc. and, affiliated institution members of non-HBCUs, like Purdue University, Carnegie Mellon University, North Carolina State University, Pennsylvania State University, University of Illinois Urbana-Champaign, University of Florida, Marquette University, East Carolina University, Western Carolina University. Also, the industrial members will be invited to join the consortium including but not limited to DPR Construction, Alberici Constructors, Samet Corporation, Brasfield & Gorrie, Power Construction, Autodesk, and Trimble.

The HBCU-IBCC will facilitate the exchange of information, resources, and best practices among the institution members, affiliated institution members, and industrial members and foster partnerships and collaborations for the future grant proposals development and implementation of the VDC curriculum and course modules. The HBCU-IBCC will also engage in future research to promote underrepresented minority students to pursue infrastructure and building construction advanced degrees through the pipeline of K-12 schools, community colleges (associate degree), and HBCUs (bachelor's, master’s, and doctorate).

Funded by NSF award # 2332003, “Planning: Using Emerging Technologies for Increasing Student Enrollment and Learning in Infrastructure and Building Construction Technology at HBCUs.” The committee gratefully acknowledge NSF’s support. Any opinions, findings recommendations, and conclusions in this document are those of the committee, and do not necessarily reflect the views of NSF and North Carolina A&T State University.

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