Sensor Networks, Computer Imaging, and Unit Influence Lines for Structural Health Monitoring: Case Study for Bridge Load Rating

Abstract

In this paper, a novel methodology for structural health monitoring of a bridge is presented with implementations for bridge load rating using sensor and video image data from operating traffic. With this methodology, video images are analyzed by means of computer vision techniques to detect and track vehicles crossing the bridge. Traditional sensor data are correlated with computer images to extract unit influence lines (UILs). Based on laboratory studies, UILs can be extracted for a critical section with different vehicles by means of synchronized video and sensor data. The synchronized computer vision and strain measurements can be obtained for bridge load rating under operational traffic. For this, the following are presented: a real life bridge is instrumented and monitored, and the real-life data are processed under a moving load. A detailed finite-element model (FEM) of the bridge is also developed and presented along with the experimental measurements to support the applicability of the approach for load rating using UILs extracted from operating traffic. The load rating of the bridges using operational traffic in real life was validated with the FEM results of the bridge and the simulation of the operational traffic on the bridge. This approach is further proven with different vehicles captured with video and measurements. The UILs are used for load rating by multiplying the UIL vector of the critical section with the load vector from the HL-93 design truck. The load rating based on the UIL is compared with the FEM results and indicates good agreement. With this method, it is possible to extract UILs of bridges under regular traffic and obtain load rating efficiently.