A Webinar – Multi-Modal Data Analytics for Predictive Human-AI Collaboration in Civil Infrastructure Systems

Civil infrastructure systems—from bridges and water utilities to airport and highway operations—demand safe, resilient, and adaptive management in environments where uncertainty, time pressure, and system interdependence often challenge human performance. This presentation introduces a multi-modal data analytics framework for predictive human-AI collaboration that integrates sensor data, human behavioral traces, and simulation feedback to enhance safety, consistency, and situational awareness across infrastructure domains. By combining human expertise with machine intelligence, the framework enables AI systems to learn from real-world decision patterns while providing interpretable, context-aware support for operators and inspectors.

Case studies will highlight applications in bridge inspection, water system monitoring, and airport operations, showing how cognitive and behavioral modeling can reveal variability in human strategies and inform adaptive AI assistance. For bridge and water systems, computer vision, augmented reality, and virtual inspection tools demonstrate how inspectors’ visual and tactile cues can be transformed into explainable digital insights to guide maintenance and asset management. In airport and transportation operations, predictive analytics using Bayesian inference and recurrent neural networks forecast high-risk events such as loss-of-separation or flow bottlenecks, supporting proactive intervention. Together, these approaches establish a scalable foundation for human-AI teaming in civil infrastructure—improving reliability, trust, and resilience across the full infrastructure life cycle.

Presenter

Dr. Pingbo Tang is an Associate Professor of Civil and Environmental Engineering at Carnegie Mellon University, where he leads research on Human-AI collaboration and multi-modal data analytics for resilient civil infrastructure systems. His work integrates computer vision, simulation, and human systems engineering to improve decision-making in domains such as bridge inspection, water system operations, airport traffic control, and highway construction. By modeling human cognition and operational behavior, Dr. Tang develops AI-enabled inspection and training systems that enhance consistency, safety, and resource efficiency in infrastructure management.

He has published over 160 peer-reviewed papers and leads several federally funded projects supported by NSF, DOT, DOE, and NASA. His innovations—including AR-guided inspection tools, sensor-integrated digital twins, and predictive analytics for infrastructure operations—advance both scientific understanding and workforce development. Dr. Tang is a recipient of the NSF CAREER Award and the ASCE Daniel W. Halpin Award for Scholarship in Construction, and serves as Associate Editor of the ASCE Journal of Computing in Civil Engineering and a founding editor of Journal of Developments in the Built Environment (Elsevier).

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