Multi-sensor and Multi-frequency Data Fusion for Structural Health Monitoring

The increasing need to evaluate the health state of existing bridges has pushed the researchers towards the study and development of innovative monitoring approaches. Among these, the high frequency GNSS (Global Navigation Satellite Systems) receivers have the potential to be a valuable support for the monitoring of structural displacement. Displacement data obtained from GNSS receivers can be combined and integrated with data measured from other sensors according to data fusion techniques in order to achieve a deeper knowledge of the structural behavior. In this context, the present paper investigates the potential of data fusion for the structural health monitoring by combining GNSS data with measures acquired with a traditional accelerometer-based monitoring system. The adopted data fusion approach is based on the Kalman filter. Structural displacements can be estimated from measured accelerations through a double integration procedure which, however, can introduce non-removable errors. Displacements measured by the GNSS receiver, although acquired with sampling rates lower than those of traditional monitoring systems, can be employed to adjust the post processed displacements and remove the uncertainties introduced with the integration procedure. Furthermore, the integration of measured accelerations and GNSS data holds the potential to identify residual displacements, which are often challenging to detect through acceleration post-processing alone. The effectiveness of this data fusion approach is examined with reference to the case study of a steel footbridge.