Development of a Bayesian Framework for Kinematic Data Fusion

Abstract

Structural health monitoring (SHM) is widely used for assessing the condition of bridges at risk. Traditional SHM techniques rely on point-wise information provided by individual sensors placed at strategic locations. However, a more comprehensive assessment of the bridge state can be achieved through data fusion, integrating information from different sensors. This article presents a Bayesian framework data fusion method that combines information from various measurements to improve the knowledge of the structural deformation state. The proposed framework identifies key deformation parameters by exploiting a simplified model that describes the system deformation state and uses an extensive set of data, including prisms, extensometers, tiltmeters, and beyond. Moreover, this approach provides a continuous knowledge of the deformation state, and reduces the uncertainties associated with individual sensor measurements. The framework developed is initially applied to a simulated case study of a simply supported beam, and then to the Colle Isarco viaduct, a highway bridge equipped with an extensive monitoring system.