Information technologies are increasingly allowing for advances in monitoring and analysis of structural response. An integrated structural health monitoring analysis framework encompassing data acquisition, database archiving, and model-free/model-based system identification/data mining techniques has been created toward the development of practical decision-making tools. Bridge testbeds at UC San Diego are serving as an environment for the development of such integrated structural health monitoring technologies.
Instrumentation includes accelerometers and strain gages for measuring the bridge spatial response, as well as video cameras for tracking the related vehicle traffic. A hardware and software setup records synchronized video and sensor data, and allows real-time Internet transmission and data archiving. Image processing techniques are used to translate the recorded video into corresponding load time histories. Machine learning techniques are employed to correlate the input traffic excitation to the output bridge response. Anomalies in this correlation may be used as a basis for structural health monitoring and related decision making applications (http://healthmonitoring.ucsd.edu).