In the design of offshore wind turbine generator (WTG) support structures, a large number of environmental parameters are used. The most important ones include the wind climate (wind speed and wind direction probability distributions), the wave climate (wave height and wave direction as well as peak frequencies of the waves, and water levels) and the soil conditions. These data in combination with the operational data of the wind turbine, such as the rotor speeds, pitch angle and yaw angle are used in dynamic simulations to determine the loads on the structure. Combining all these data correctly ensures that the support structure design, which is subject to large dynamic loads, is fitting for each specific location of the WTG. In particular, the environmental conditions should not coincide too much/often with the support structure natural frequencies, as that may result in resonance, thereby increasing fatigue life consumption. The dynamic behavior of these offshore support structures is of eminent importance. During the operation of WTGs, the actual dynamical properties of the WTG can be compared to the design. There are several reasons behind the goal of this comparison, for example verifying if the loading assumptions in the design were correct, assessing whether the life time can be extended or even whether the structure can be optimized (e.g. better foundations). Typically, this is done by comparing acceleration measurements of the WTG during idling (e.g. non-operational) conditions and applying Operational Modal Analysis algorithms. Given the high availability of modern wind turbines (e.g. >95% of time in operation) such measurements cannot be done as often as desired for this purpose. Therefore, the question arises to what extent the dynamical properties of WTGs can be estimated using measurement data obtained during operational (operational) conditions. This research explores the applicability of OMA algorithms in estimating the dynamical properties: the natural frequencies, the mode shapes and modal damping for operational conditions of offshore wind turbines on monopile foundations, using the Luchterduinen offshore wind park as a case study. The OMA technique explored in this thesis is Eigensystem Realization Algorithm. Test cases were used to confirm the performance of ERA for the estimation of the dynamic parameters. Stability diagrams were used to identify poles for stable modes (frequencies) in the frequency spectrum of data samples and to estimate a correct size of the Hankel matrices. It is concluded that this preprocessing and pre-analysis of the results is important to confirm the performance of ERA to correctly identify stable modes of support structures of operational offshore WTGs.Overall, it is concluded that ERA can yield useful results in estimating the natural frequencies for both ON and OFF, provided that an automated analysis is combined with a visual analysis. Even then, the accuracy and the consistency of the identified frequencies is moderate. ERA does not perform well for estimating the mode shapes, yielding inaccurate and inconsistent results. Based on the results of this work, ERA is found not to be capable of estimating modal damping ratios. The consistency for the mode shapes is not in all cases good and further study is recommended for both the mode shapes and the modal damping ratio. Another recommendation is to further study the size of Hankel matrices for this application. Furthermore, it is advised to also investigate the use of more advanced Operational Modal Analysis algorithms. The results of this study may be used to explore differences between wind turbines with and without scour protection and also to compare identified dynamic parameters to values used in the design.

In the Netherlands there are many bridges that crosses waterways, which are key nodes in the transportation system. The safety and integrity of these bridges is nowadays assessed by (visual) inspections at regular intervals. This approach cannot provide information about damage development in between inspections, leading to potential failure or high intervention costs. A maintenance strategy for bridges based on continuous vibration-based monitoring provides a possible cost-effective solution that may replace the periodic (visual) inspections. Vibration-based structural health monitoring of civil engineering structures is receiving increasing attention in recent years. This is due to new developments in related areas such as sensing technology, system identification, data mining and condition assessment. A particular damage scenario that frequently occurs in steel orthotropic deck structures is the development of fatigue cracks in the welds between the longitudinal U-shaped stiffeners and the transverse beams or the steel deck plate. This damage scenario was also observed in the steel deck structure of the Zwartewaterbrug, located in Hasselt in the Netherlands. Vibration tests were performed on the bridge to develop a damage detection algorithm for early detection of the fatigue cracks in the bridge deck. In order to simulate the effect of damage, small masses were attached to the bottom of the bridge deck. The measurements were performed over a relatively short time period to exclude – in as far as possible – the effects of environmental variability. However, the bridge was kept open to traffic during the testing and structural response was primarily triggered by traffic load. The vibration data (accelerations) obtained during these tests was used for validation of the proposed methodology in this thesis. The aim of the thesis is to detect the added masses from the vibration data. To achieve this, a data-based approach for damage detection was proposed, which, besides the data acquisition, consists of four main stages: data preprocessing, feature extraction, pattern recognition and decision making. The analysis is focused on the high-frequency acceleration response, because the associated higher-frequency local modes are more sensitive to the small changes induces by the added mass. The similarity filtering preprocessing procedure was applied to filter out the variability related to the operational loading, and the Frequency Domain Decomposition system identification technique was used to extract damage indicators. Subsequently, a support vector machine algorithm was applied to “learn” patterns in the extracted damage indicators, which enables the algorithm to assign a damage state to a given measurement vector. Alternatively, a novelty detection technique was used to distinguish data from the “healthy” structure from data corresponding to a “damage” state. It was not possible to reliable detect the added masses from the high-frequency acceleration data due to the large variations in the damage indicators of a mass class. These variations are attributed to the changing environmental conditions over the measurement period. The principal component analysis was used in an attempt to remove these variations from the damage indicators, but the results were still not satisfactory. In conclusion, it was not possible to properly test the proposed methodology for damage detection using the data of the Zwartewarterbrug, mainly because the data was not suitable for the machine learning algorithms employed. Recommendations are given related to the methodology for the similarity filtering, determination of the high-frequency range, and machine learning algorithm to be used. Additionally, recommendations are given for future measurement campaigns to obtain data that is more suitable for successful application of the methodologies employed in the present project.