Integration of computer imaging and sensor data for structural health monitoring of bridges
Abbreviated Journal Title
Smart Mater. Struct.
Instruments & Instrumentation; Materials Science, Multidisciplinary
The condition of civil infrastructure systems (CIS) changes over their life cycle for different reasons such as damage, overloading, severe environmental inputs, and ageing due normal continued use. The structural performance often decreases as a result of the change in condition. Objective condition assessment and performance evaluation are challenging activities since they require some type of monitoring to track the response over a period of time. In this paper, integrated use of video images and sensor data in the context of structural health monitoring is demonstrated as promising technologies for the safety of civil structures in general and bridges in particular. First, the challenges and possible solutions to using video images and computer vision techniques for structural health monitoring are presented. Then, the synchronized image and sensing data are analyzed to obtain unit influence line (UIL) as an index for monitoring bridge behavior under identified loading conditions. Subsequently, the UCF 4-span bridge model is used to demonstrate the integration and implementation of imaging devices and traditional sensing technology with UIL for evaluating and tracking the bridge behavior. It is shown that video images and computer vision techniques can be used to detect, classify and track different vehicles with synchronized sensor measurements to establish an input-output relationship to determine the normalized response of the bridge.
Smart Materials & Structures
"Integration of computer imaging and sensor data for structural health monitoring of bridges" (2010). Faculty Bibliography 2010s. 1007.