Pingbo Tang
· Associate ProfessorVerifiedCarnegie Mellon University · Civil and Environmental Engineering
Active 1989–2026
About
Pingbo Tang is an associate professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University. He founded and directs the Spatiotemporal Workflows and Resilient Management Laboratory (SWARM Lab). His educational background includes a bachelor's degree in civil engineering and a master's degree in bridge engineering from Tongji University in China, obtained in 2002 and 2005 respectively, and a Ph.D. from Carnegie Mellon University in 2009. His research explores the application of remote sensing, human systems engineering, data analytics, and information modeling technology to support spatiotemporal analyses for predictive management of constructed facilities, workspaces, and civil infrastructure systems. His ongoing studies focus on sensing and modeling methods for understanding Human-Cyber-Physical-Systems (H-CPS) in accelerated construction and infrastructure operations, such as airport operations and nuclear plant outage control. Tang has published over 100 peer-reviewed articles in these areas and has received funding from organizations including the NSF, DOE, NASA, Salt River Project, and Phoenix Government. He holds leadership roles and memberships in professional societies such as the American Society of Civil Engineers (where he is Chair of the Data Sensing and Analysis committee), TRB, IABSE, ASPRS, and ASTM International. Additionally, he serves on the editorial board of the ASCE Journal of Computing in Civil Engineering and is an active reviewer for top journals and conferences related to computing in civil engineering. His contributions have been recognized through awards including best paper awards at major conferences, the Construction Industry Institute's best poster award, the Civil and Environmental Engineering Department's Recent Alumnus Achievement Award at Carnegie Mellon, and the NSF CAREER Award in 2015.
Research topics
- Computer Science
- Artificial Intelligence
- Engineering
- Data Mining
- Computer vision
- Medicine
- Operations research
- Mathematics
- Industrial engineering
- Human–computer interaction
- Remote sensing
- Geology
- Operations management
Selected publications
Analysis of Drone-Assisted Building Inspection Training in VR vs. 2D Monitor Display: An EEG Study
2026-01-28
articleResearchers have been using simulation-based methods for drone-assisted inspection training. Multiple brain regions are associated with information processes and decision-making, and the connectivity of these regions may further influence inspectors’ performance. However, researchers do not understand the pathways of the information flows when drone pilots process the maintenance and manipulation of information, which may affect the efficiency of tacit knowledge transfer. This study aims to reveal the causal connection between participants’ brain regions using an electroencephalogram and dynamic causal modeling when processing drone-assisted building energy audit tasks using different display modalities. The results showed similar single-direction connectivity patterns for the different simulation groups. The results also showed similar patterns between brain regions related to visual inspection performance before and after training. These findings highlight the nature of brain asymmetries and may be utilized in measuring cognitive states and designing adaptive automation in the knowledge transfer of drone-based inspection.
ArXiv.org · 2026-01-02
articleOpen accessThe Architecture, Engineering, and Construction (AEC) industry is undergoing rapid digital transformation, producing diverse digital assets such as datasets, computational models, use cases, and educational materials across the built environment lifecycle. However, these resources are often fragmented across repositories and inconsistently documented, limiting their discoverability, interpretability, and reuse in research, education, and practice. This study introduces OpenConstruction, a community-driven open-science ecosystem that aggregates, organizes, and contextualizes openly accessible AEC digital resources. The ecosystem is structured into four catalogs, including datasets, models, use cases, and educational resources, supported by consistent descriptors, curator-led validation, and transparent governance. As of December 2025, the platform hosts 94 datasets, 65 models, and a growing collection of use cases and educational materials. Two case studies demonstrate how the ecosystem supports benchmarking, curriculum development, and broader adoption of open-science practices in the AEC sector. The platform is publicly accessible at https://www.openconstruction.org/.
2026-01-28
articleSenior authorIn workforce training for manufactured construction, managers have relied on their tacit knowledge to manage workers’ learning curves, resulting in redundancies and less effective skill acquisition. While previous studies demonstrate that skill acquisition is more effective when tasks align with prior knowledge, less attention has been given to identifying similarities across different workflows. This study proposes a framework for enhancing skill acquisition by measuring task similarity between workflows. Using operation manuals, we represented a knowledge graph and vectorized each item to calculate similarity using a large language model (LLM). Validation was conducted using standard operating procedures (SOPs) from two different heating, ventilation, and air conditioning (HVAC) product production lines. This study demonstrates that items with specific roles exhibit higher similarity than those with general roles and highlights the potential to identify the most similar task in different workflows. The findings can guide research on identifying tasks requiring focused training.
arXiv (Cornell University) · 2026-01-02
preprintOpen accessThe Architecture, Engineering, and Construction (AEC) industry is undergoing rapid digital transformation, producing diverse digital assets such as datasets, computational models, use cases, and educational materials across the built environment lifecycle. However, these resources are often fragmented across repositories and inconsistently documented, limiting their discoverability, interpretability, and reuse in research, education, and practice. This study introduces OpenConstruction, a community-driven open-science ecosystem that aggregates, organizes, and contextualizes openly accessible AEC digital resources. The ecosystem is structured into four catalogs, including datasets, models, use cases, and educational resources, supported by consistent descriptors, curator-led validation, and transparent governance. As of December 2025, the platform hosts 94 datasets, 65 models, and a growing collection of use cases and educational materials. Two case studies demonstrate how the ecosystem supports benchmarking, curriculum development, and broader adoption of open-science practices in the AEC sector. The platform is publicly accessible at https://www.openconstruction.org/.
2026-01-28
articleAccurately capturing and explaining material handling strategies in civil engineering projects could help to increase safety, quality, timely delivery, as well as understanding the material waste caused by rework and environmental condition changes, especially when humans are highly involved in tasks with complex geometries. Workers must adapt their strategies based on the working environment’s characteristics, leading to substantial differences in strategies depending on context which drives downstream inefficiencies. A particularly challenging area is the handling of composite materials, as they require substantial manual forming which is inherently error prone. This work seeks to model and understand the composite material handling process. Specifically, this paper aims to enable a reinforcement learning (RL) agent to emulate humans’ adaptive material handling process through layup process monitoring and control strategies. The taxonomy system of edge conditions and actions in the paper contributes to the description of the module configuration and controls the RL model with a small search space. Predictions of the policies including the layup sequences and action are utilized by the students to perform a manual layup on the module. Thus, integration of machine-predicted strategies into human operations shows an example of human–machine collaboration, bridging the gap between human intuition and machine’s predictions. The mean total shear, wrinkled area, and total fold were reduced from 56.31% to 50.37%, 21.07% to 17.74%, and 23.80% to 22.66%, respectively, through human–machine collaboration. The policy can also be transferred to different module configurations in future works to enhance humans’ work to adapt to various environments.
Advanced Engineering Informatics · 2026-04-11
articleOpen accessSenior authorAdvanced Engineering Informatics · 2026-01-28
articleOpen accessCorrespondingGenerative artificial intelligence (GAI) has the potential to reshape workflows across the Architecture, Engineering, and Construction (AEC) sector. While previous research has offered valuable technical demonstrations and conceptual analyses, empirical evidence quantifying GAI-related impacts across AEC occupations and systematic assessment of adoption readiness remain limited. This study develops a domain-specific socio-technical evaluation framework that provides occupational-level analysis of technical capabilities, social risks, and adoption barriers across thirteen O*NET-defined AEC occupations. Data were collected through a six-month survey of 162 AEC professionals, complemented by six expert interviews and a systematic literature review. The findings reveal: (1) Technical Capability , measured using exposure scores ranging from −1 (low applicability) to +1 (high applicability), shows moderate applicability in design-oriented roles (e.g., architectural drafters: 0.16) and minimal alignment for site-based and manual activities (e.g., construction laborers: −0.89). (2) Social Risks , assessed on a 0–1 scale of concern, identify hallucinations (0.71), data privacy (0.70), and intellectual property issues (0.69) as critical concerns. (3) Socio-Technical Adoption highlights limited technical expertise (26.0%) and uncertain return on investment (16.8%) as primary barriers, while respondents emphasized the need for usage guidelines and standards (29.6%) and targeted training (29.2%) to facilitate responsible integration. Based on these findings, the study outlines strategic priorities for responsible GAI deployment, including AEC-specific standards, targeted workforce training, human-in-the-loop validation mechanisms, and domain-tailored digital infrastructure. The framework and empirical evidence provide a foundation for researchers, practitioners, and policymakers seeking to guide the safe and effective integration of GAI into AEC workflows. • A socio-technical evaluation framework tailored to AEC GAI integration. • Task- and occupation-level analysis of capability readiness across thirteen O*NET roles. • Quantitative assessment of risks, adoption barriers, and organizational support needs. • Evidence-based recommendations on guidelines, training, human oversight, and digital infrastructure.
2026-01-28
articleInconsistencies in inspection outcomes, whether by human inspectors or AI systems, pose significant safety risks for heavy-duty vehicles (HDVs). Human inspectors face challenges due to complex vehicle states, while AI systems struggle with sparse historical data. These limitations contribute to critical safety issues. Brake failures cause 29% of HDV crashes, and 15.6% of crashed HDVs in 2021 had brake inspection violations. Inspectors, using tacit knowledge, determine where to focus, when to stop collecting information, and how to interpret it to make decisions. To study this process, authors surveyed 20 participants over 5 inspection rounds, evaluating 8 vehicles per round with brake condition ground truths, and applied a statistical framework to analyze participants’ feature selection and learning patterns. Results showed participants prioritized brake pad thickness (77%) and vehicle age (72%), aligning with expert-identified factors. These findings highlight the value of integrating human heuristics into AI systems for more reliable HDV assessments.
2026-01-28
articleSenior authorFrequent manufacturing changeovers in customized building component production create long time and material waste as operators struggle to identify proper process parameters for diverse product variants. Although human operators possess contextual knowledge, such as how material behavior impacts product qualities, the vast configuration space and uncertainties often exceed what purely manual trial and error can efficiently handle. Meanwhile, advanced artificial intelligence (AI)-based methods (e.g., Bayesian optimization and reinforcement learning) promise to automate parameter tuning but may overlook subtle domain insights crucial for achieving efficient changeovers. To address these challenges, this study proposes a human–AI collaboration strategy that combines the intuitive expertise of operators with data-driven algorithms. Humans guide the algorithms by providing informed priors and explicit domain rules, constraining the search space, and expediting process tuning. Conversely, AI explores beyond human intuition to refine and validate these parameters under different operating scenarios. Tested on the developed interactive simulator ProSimX, our findings highlight the potential of the human–machine collaboration strategy by merging operator knowledge with algorithmic power, offering a pathway to improved quality and efficiency in building component production.
Multi-dimensional data interpretation for defective filter identification
Smart Construction · 2025-06-20
articleSenior author
Recent grants
Frequent coauthors
- 61 shared
Jinding Xing
- 54 shared
Cheng Zhang
Ministry of Natural Resources
- 36 shared
Hubo Cai
China Electric Power Research Institute
- 35 shared
Alper Yılmaz
- 35 shared
Ronald L. Boring
Idaho National Laboratory
- 32 shared
Zhe Sun
Eli Lilly (United States)
- 32 shared
G. Edward Gibson
Idaho National Laboratory
- 29 shared
Yanyu Wang
Labs
HMHIPI
Human-Machine Harmony for Infrastructure
Education
- 2002
B.S., Civil Engineering
Tongji University, Shanghai, China
- 2005
M.S., Bridge Engineering
Tongji University, China
- 2009
Ph.D.
Carnegie Mellon University
Awards & honors
- National Science Foundation CAREER Award (2015)
- 2013 Recent Alumnus Achievement Award of the Civil and Envir…
- Robert E.. Uhrig Graduate Scholarship by the Human Factors,…
- Best paper awards on top conferences (2019 ASCE Internationa…
- Best poster award of Construction Industry Institute's 2011…
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