GlobVision developed analytical, statistical and probabilistic methodologies and data-driven modeling techniques for the interpretation of bridge sensor data. The work makes it possible to monitor structural components and estimate their remaining useful life. Through behaviour modeling and performance assessment of the structure, highly improved health monitoring and anomaly detection using existing bridge instrumentation data was achieved. Moreover, the same techniques produced significant advancements in evaluating the impact of extreme events (e.g. earthquakes) on bridge performance and safety.
Those solutions were integrated into an operational software tool which results in substantial increase in the efficiency, accuracy and reliability of health monitoring and safety assessment of bridges. By providing better information, GlobVision enables condition-based maintenance and predictive maintenance scheduling of bridges, which highly improves bridge maintenance and safety, and avoids unnecessary/unplanned interruptions of bridge operation, resulting in significant cost reductions.