Vibration-based structural health monitoring of civil engineering structures is receiving increasing attention in recent years. This is due to the development of more robust system identification techniques as well as improvements with regard to the practicality of installing the necessary instrumentation. Health monitoring systems are more often replacing static deflection tests and detailed visual inspections, where continuous monitoring of vibration data aims for early damage detection.

This thesis can be summarized as the development of a structural health monitoring method for bridges. It forms a part of the Zwartewaterbrug project, related to the Zwartewater bridge located in the city of Hasselt, in the Dutch province of Overijssel. The Zwartewater bridge served as a case study for validation of the proposed early damage detection algorithm.

The wider purpose of this work was to address the problem of having a large number of bridges in Europe that are facing the end of their service life and should either be retrofitted or decommissioned.. The chosen approach consisted of a low-cost vibration monitoring method, that is suitable for continuous structural health monitoring.

The initial challenge was to introduce a small change to the structure, representative of early damage, without actually damaging the structure. For this an added mass approach was taken, where weights of respectively 25, 50, 75, and 100 kg were added below the bridge deck. The vibration data (accelerations) measured on the ”damaged” structure were then used to solve an inverse problem where the aim was to detect the induced damage without closing the bridge to traffic.

Since the data was gathered with only ambient excitation by wind and traffic, a new challenge arose, which is considered to be the core of this thesis. The structural differences between vibration measurements related to the ”healthy” and ”damaged” structure were concluded to be smaller than the structural changes due to the differences in unknown traffic loading. The traffic loads were namely varying from 200kg motorcycles to 50t trucks.

In this thesis, the method of Probabilistic Filtering was developed to face the aforementioned challenge. The method involves mainly a data pre-processing step, and the probabilistically filtered signals are subsequently processed with two output-only identification methods: Frequency Domain Decomposition and data-driven Stochastic Subspace Identification. The full process was integrated into an automatic analysis algorithm, performed in MatLab.

It was concluded that both detection and localization of the induced damage is possible with the proposed methodology. Further work was then performed to quantify the detected added mass in terms of structural damage. A quantitative finite element analysis was performed with the substructure approach (super-element, static boundary conditions to the substructure of interest). The given analysis concluded that the developed structural health monitoring method is able to detect anomalies comparable to a 35cm crack in the welds securing the longitudinal stiffeners to the transverse beams.

Finally, recommendations were made regarding an expansion of the method to structure service life predictions and more computationally efficient calculation set-ups.