VINES: Track 1: U.S-Ireland R&D Partnership: Synchronized Mobile and Stationary Sensor Networks for Real-Time Bridge Health Assessment (SYN-Bridge)

Bridge infrastructure forms a critical foundation of modern society and the economy, supporting the movement of people, goods, and emergency services while connecting communities across the nation. The resilience of transportation systems depends on the ability of infrastructure to adapt to changing conditions and to function effectively within complex, interconnected environments. Yet many aging bridges are inspected only periodically and often require costly and disruptive manual evaluations. This international collaborative project brings together investigators from the University of Alabama at Birmingham, University College Dublin, and Queen's University Belfast to develop new methods for assessing bridge safety more frequently and in greater detail. Instead of relying solely on crews working directly on the structure, the project uses instrumented vehicles, unmanned aerial vehicles, and bridge-mounted sensors to collect information while traffic continues to flow. By combining these measurements in a synchronized and intelligent manner, the SYN-Bridge project seeks to provide a clearer and more complete understanding of bridge condition and performance over time. SYN-Bridge advances fundamental knowledge in structural health monitoring, wireless sensing, artificial intelligence, robotics, and digital twin technology for bridges. Research activities are organized around the development of synchronized sensing methods that accurately align measurements collected by vehicles, aerial systems, and bridge-mounted devices in real time. Advanced signal processing, machine learning, probabilistic modeling, and decision-making frameworks will be developed to identify, locate, and quantify structural damage under realistic traffic and environmental conditions. The project will also develop strategies to coordinate fleets of unmanned aerial vehicles for targeted inspections and to continuously update digital twin models of bridges as new information becomes available, enabling near-real-time condition assessment and decision support. The project will contribute to safer and more resilient transportation infrastructure by enabling earlier detection of structural problems and faster assessment following natural hazards and other extreme events. Remote and automated inspections can reduce the need for workers to access hazardous locations while lowering maintenance costs and minimizing traffic disruptions. Educational activities will provide interdisciplinary training opportunities for undergraduate and graduate students in civil engineering, robotics, artificial intelligence, and data science. Outreach efforts will include workshops, demonstrations, open educational resources, and publicly accessible data and software tools to encourage wider participation in science and engineering. By creating new connections among various sensing technologies and integrating information from mobile and stationary platforms, the project has the potential to transform infrastructure monitoring. This connected framework will reveal previously inaccessible relationships among data sources and support more informed, timely, and cost-effective decision making by transportation agencies, local governments, and infrastructure owners, ultimately enhancing public safety, infrastructure sustainability, and community resilience. This project was submitted under the United States-Ireland-Northern Ireland R&D Partnership and is a collaboration between researchers at the University of Alabama, University College Dublin, and Queen's University Belfast. 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.