Structural health monitoring using video stream, influence lines, and statistical analysis

Abstract

Civil infrastructure systems experience damage, overloading, aging due to normal operations, severe environmental conditions, and extreme events. These effects change the structural behavior and performance. Novel structural health monitoring (SHM) strategies are increasingly becoming more important to objectively determine the actual condition and these changes. The main objective of this study is to demonstrate the integration of video images and sensor data as promising techniques for the safety of bridges in the context of SHM. The UCF 4-span bridge model is used to demonstrate the method. Image and sensing data are analyzed to obtain unit influence line (UIL) as an index for monitoring the bridge behavior under loading conditions identified using computer vision techniques. UILs are extracted for several different moving loads. In addition to the analysis of UILs in a comparative fashion, a new method based on statistical outlier detection from UIL vector sets is proposed and demonstrated. The new method is applied to detect and identify some of the most common damage scenarios for bridges such as changes in boundary conditions and loss of connectivity between composite sections. Successful results are obtained from the experimental studies.