Exploiting traffic-induced vibration for high-resolution damage identification in bridges with sparse instrumentation

Growing traffic demand on aging civil infrastructure raises the need for reliable structural health monitoring tools for "minor" bridges. Data management can be power-demanding in state-of-the-art dense wireless sensor networks. This study presents an original damage identification algorithm for viaducts to identify a dense curvature distribution of the loaded structure based on sparse acceleration recordings. Moving vehicles are exploited to obtain dense spatial information of the structural features and localize anomalies with high resolution. Modal parameters and curvature influence lines are obtained by pro- cessing the raw acceleration signals with a particular filter bank, efficiently implementable through edge computing technologies.