Bridge Monitoring Using Vehicle-Induced Vibration

Due to growing traffic demand, aging civil infrastructure raises the need for reliable tools to monitor structural health conditions, usable to plan informed maintenance and emergency management. Several structures with historical and monumental importance are instrumented with structural health monitoring (SHM) systems nowadays. However, even the failure of "minor" viaducts could endanger the safety of travelers and goods. Lately, dense wireless sensor networks (WSNs) based on MEMS devices are used to cut costs and simplify the deployment of SHM systems while collecting as much information as possible. However, dense WNSs are affected by data management, synchronization, and battery replacement issues, which make them unappealing for widespread use. This study presents an original damage identification algorithm based on sparse sensor networks. Traveling vehicles are exploited to obtain spatial information and accurately identify the location of structural anomalies. The curvature influence line of the monitored bridge can be calculated by processing the acceleration response measured at a given instrumented location through a low-pass filter. In this procedure, sensors operate individually, not needing energy-consuming synchronization. The proposed identification algorithm is verified on real data collected on a steel truss bridge subject to artificially induced damage