Collaborative Research: REU Site: Research Experience in Digital Twins of Road Infrastructure

About This Grant

This REU program addresses critical national challenges related to the aging highway infrastructure and limited maintenance funding by preparing undergraduate students to apply digital technologies to infrastructure engineering. Focusing on digital twins—virtual models that mirror physical road assets—the program equips students with the knowledge, skills, and tools to improve infrastructure monitoring, decision-making, and long-term resilience. It supports NSF’s mission by advancing science, promoting national welfare, and developing a skilled STEM workforce capable of leading digital innovation in infrastructure. Through hands-on research, mentorship, and international collaboration, students gain interdisciplinary experience that blends engineering, computing, and data science. The program also broadens access to emerging research areas and prepares participants for graduate study and future careers in infrastructure systems. The objective of this REU site is to engage U.S. undergraduate students in interdisciplinary research on digital twins for road infrastructure. Over three summers, 24 students from West Virginia University, the University of Wisconsin–Madison, and nearby institutions will participate in a 10-week program—eight weeks at U.S. host institutions, followed by two weeks at the University of Cambridge’s Laing O’Rourke Center. Students will conduct research on data acquisition, modeling, simulation, and decision-support tools for digital replicas of road assets. Activities will address challenges such as creating scalable models, integrating sensor data, and validating digital twin outputs for infrastructure monitoring and maintenance planning. The program combines civil engineering, computing, and data science to expose students to real-world infrastructure systems and emerging digital technologies, while fostering transatlantic collaboration and broadening their academic and professional perspectives. This project is jointly funded by the Division of Engineering Education and Centers (EEC) and the Division of Civil, Mechanical and Manufacturing Innovation (CMMI). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.