Structural Health Monitoring For Damage Detection Based On Integration Of Computer Imaging And Sensor Data

Novel structural health monitoring strategies for better management of civil infrastructure systems( CIS) are increasingly becoming more important as CIS structural performance decreases due to reasons such as damage, over loading, severe environmental conditions, and aging due to normal continued use. In this study, integration of imaging and optical devices with traditional sensing technology for damage detection is explored and demonstrated in the UCF 4-span bridge model. Two different remote controlled vehicles under various loading scenarios crawl over the bridge while a video camera supervises the structure providing traffic video stream. At the same time, a distributed array of sensors collects data. Correlation between moving vehicular load and structural responses is determined as Unit Influence Lines (UIL) are extracted and used as an index for monitoring the bridge behavior. Some of the most common damage scenarios such as rusted supports, stiffness reduction and loss of connectivity between composite sections are experimentally simulated, processed, and analyzed by means of statistical methods. Outlier detection algorithms are used to identify changes in the structure. Finally, damage detection results are presented and discussed. © 2010 Taylor & Francis Group, London