Friction damping is common in engineering structures for the purpose of energy dissipation and vibration control. Examples of applications are bolted connections and earthquake isolation systems. However, there is a shortage of works that investigate the effects of different contact materials on the energy dissipation performance and friction behaviour of friction dampers, which is valuable knowledge for design optimization.

This thesis uses a numerical approach to explore the time response and energy dissipated by friction of the harmonically excited SDOF (single-degree-of-freedom) system with Coulomb friction contact between the sliding mass and a fixed wall. In addition, for the same SDOF system, an experimental investigation of the friction damping performance in terms of friction behaviour and energy dissipation is carried out for (1) steel, (2) rubber and (3) aramid contact
materials. The aim is to get a better understanding of how different contact materials affect the performance of friction dampers. Various time scales, excitation frequencies and friction forces are considered.

The main findings of this research are: (1) the characterization of the friction behaviour of steel-to-steel, rubber-to-steel and aramid-to-steel contacts; (2) the comparative analysis of the energy dissipation performance of the different contact materials; (3) the assessment of the long-term performance of the different contacts; (4) the comparison between numerical results based on the Coulomb friction model and experimental results. The tests have shown that rubber has the highest energy dissipation capacity and fairly unstable behaviour, steel has the second highest energy dissipation and irregular behaviour and aramid has the lowest energy dissipation performance and very consistent behaviour. Finally, the application of a method that calculates the energy dissipation of friction damping based on direct experimental outputs is an important contribution to the field regarding experimental investigations.

Two-bladed offshore wind turbines regained interest in finding the most profitable way of generating wind energy. Wind industry companies demand the safe operation of two-bladed offshore wind turbines. To guarantee safe operation, the companies perform operational modal analyses to investigate modal properties variation which might be allocated to damage. However, the operational modal analysis of an operative two-bladed offshore wind turbine faces multiple challenges. (1) Fundamental operational modal analysis assumptions about the applied loads are violated by environmental and operational loads. (2) The closely spaced modes of an offshore wind turbine are hard to identify. (3) An operative two-bladed offshore wind turbine is a linear time-variant system. This paper introduces an enhanced operational modal analysis procedure to overcome some of the mentioned challenges. The enhanced procedure incorporates a post-processing technique in a transmissibility-based approach. A developed representative model of an operative two-bladed offshore wind turbine is used to compare the enhanced procedure with the frequency domain decomposition method. Based on the comparison, this paper proposes a new operational modal analysis method that combines a transmissibility-based approach, the post-processing technique, and the frequency domain decomposition method. This paper proves that this proposed combined method is a promising new operational modal analysis technique that outperforms the enhanced procedure and the frequency domain decomposition method in identifying the modal properties of a two-bladed offshore wind turbine.

Perforated monopiles show promise in providing a better alternative to the commonly used jacket-like substructures used in intermediate water depths in the range of 30 to 120 m. By introducing perforations near the vicinity of the splash zone the wave loads on the monopile can be mitigated and the fatigue damage reduced, which is the main advantage jackets have over monopiles. Furthermore, perforated monopiles would be easier to install and manufacture compared to jacket structures, which has the potential to reduce the Levelized Cost of Electricity (LCoE) considerably. Perforated monopiles also have the benefit of reduced (local) scour, reduced corrosion damage and providing a habitat for marine life.

To optimize the design of perforated monopiles fast novel surrogate models are needed to determine the (wave) load reduction by the introduction of perforations on the monopile. As traditional (full) order Computational Fluid Dynamics (CFD) models are slow and expensive to evaluate. Using a surrogate model a wider range of geometries can be used in the early design optimization steps. Promising designs can then be further evaluated using full models.

This thesis develops a surrogate model for the characterization of the (hydrodynamic) loads on perforated monopiles using Convolution Neural Networks (CNN). A dataset is created of 15,561 samples, which considers geometries of varying number of perforations, porosity and ‘angle of attacks’. A Finite Element Method (FEM) CFD model is made in Gridap for a ‘creeping flow’ to determine the flow fields. And determines a load Reduction Factor (RF) by the introduction of perforations. Furthermore, using a Signed Distance Function (SDF) a representation of the geometry is made for use in the CNN model.

Using PyTorch a CNN model is used to predict the RF for a certain input. The model combines multiple convolutional layers with an optional regression head (with linear layers). Five different models are created. Three for representing the geometry by using the SDF, decomposing the SDF in its x and y distance components and the binary representation. Furthermore, two models combine the SDF or distance components with the flow fields from the CFD model.

The results show that a speedup of 386 times is achieved by using the surrogate model, showing the main benefit of using a surrogate model. This is likely to improve considerably in further research when turbulence is taken into account. Furthermore, each of the five models show a good accuracy in predicting the RF. A general trend is observed that the more information is provided in the input to the CNN model the better the accuracy tends to be. The combination of implicit representation of the geometry by using distance components with the CFD flow fields shows the best accuracy with an expected maximum relative error rate of 0.94% within the parametric space of the dataset.

The slip joint connection is a relatively new alternative method for connecting offshore wind turbine towers to prior installed monopiles. It consists of two overlapping conical sections that together form a connection. There are several challenges left to be solved before the slip joint can be applied on a commercial scale. One of those challenges lies in the decommissioning process of the slip joint connection. It is proven that build-up settlement can increase the friction force within the joint over its lifetime and complicate its disconnection. One potential method for reducing the friction force in the slip joint is the excitation of one of the structures shell modes localized around the slip joint. Good estimations of the shell modes of the structure are therefore essential. Models for precise estimation of the shell modes of a wind turbine with a slip joint are difficult and expensive to develop due to the complexity of the joint. This thesis is a study into the possibility of using simplified finite element models to estimate the shell modes of a wind turbine connected with a slip joint. This is done by estimating the shell modes of the structure using a reference model and seven simplified models in a modal analysis. The estimated shell modes are compared based on their eigenfrequency and mode shape. Based on this comparison, conclusions about the applicability of simplified models are drawn. The first 300 eigenmodes of the structure are estimated with both the reference model and simplified models. Of these eigenmodes, ten shell modes of interest are selected based on their eigenfrequency and location at which the modes are localized. The modes of interest are than compared for their eigenfrequency and mode shape. The mode shapes are compared based on the Modal Assurance Criterion (MAC), which is calculated at a specific location of interest. In addition to this, the angle in which the modes are orientated is measured and compared, as this can be useful information for the decommissioning process. Results showed that three out of seven simplified models studied resulted in a estimation of the shell modes which was sufficiently accurate. The simplifications used for these models consist of the averaging of wall thickness for the upper and lower slip joint and a simple model for the upper slip joint. From these results it can be concluded that some of the simplifications can be applied when estimating shell modes of a wind turbine tower connected with a slip joint, as they lead to only a small decrease in accuracy of the shell mode estimation. However, using these results to make predictions about other potential simplifications is challenging as each different simplification has a unique influence on the estimation of the shell modes.

An issue of utmost significance constitutes the maintenance of engineering systems exposed to corrosive environments, e.g. coastal and marine environments, highly acidic environments, etc. The most beneficial sequence of maintenance decisions, i.e. the one that corresponds to the minimum maintenance cost, can be sought as the solution to an optimization problem. Owing to the high complexity of this sequential decision optimization problem, traditional methods such as thresholdbased approaches, fail to arrive at an optimal strategy, while at the same time the commonly used offline knowledge about the environment can not capture efficiently the stochastic way in which an engineering system deteriorates. Over the last few years, Deep Reinforcement Learning (DRL) has been proven a promising tool to tackle such problems, being often limited though by the dimensionality curse and the implications caused by large state and action spaces, an issue which leads to simplifications like their discretization. Bayesian principles and model updating are the most widely used tools to model accurately systems with high uncertainty, by incorporating data acquired through monitoring devices and thus improving the knowledge about the stochastic system.

This research proposes an integrated framework that aims to determine an optimal sequence of maintenance decisions over the lifespan of deteriorating engineering systems, combining the aforementioned core concepts of Deep Reinforcement Learning (DRL) and Bayesian Model Updating (BMU). More specifically, it investigates different Deep Reinforcement Learning (DRL) algorithms, namely Double Deep Q-Network (DDQN), Advantage Actor Critic (A2C), and Proximal Policy Optimization (PPO), while the updating of the uncertain parameters is performed through sampling, i.e. No-U-Turn Sampler (NUTS). All these tools will be first applied to elementary problems for the sake of verification and validation, while the culmination of this research is the application of the framework on a more realistic and complicated, multi-component structure. The obtained results are compared with benchmark performances to properly showcase the efficiency and the weaknesses of the tool.

In slender high-rise buildings wind-induced accelerations can become the governing design criterion. These issues can be solved by increasing mass, increasing stiffness, or increasing the damping ratio. The focus of this research is on the last option and investigates the opportunities of supplemental damping in the Dutch high-rise building context. The research report answers the following main research question: In what way can supplementary damping be applied in Dutch, slender, tall buildings to efficiently meet the structural design requirements? A literature research was conducted to the topics of wind-engineering, high-rise structures, and supplemental dampers. To answer the main research question two research methods have been applied. First, a variant study was performed in Karamba, a FEA plug-in for Rhino Grasshopper. Building height, floor mass, outrigger, stiffness limits, and damping ratio were varied. From this analysis charts were generated from which it could be determined which design requirement is governing. In this way opportunities for the application of supplemental damping could be determined. In the last phase of the research the mitigating effects of tuned mass damper (TMD) were modelled with the use of the theory of random vibrations and modal analysis. With a developed python script the reduced accelerations could be computed and the parameters of a TMD could be determined.

As the average age of bridges in the Netherlands is increasing and loads are rising, more research on the capacity of these bridges is required. One of the methods to determine if a bridge still has sufficient safety is proof load testing. Stop criteria are values of structural responses at which further loading may course irreversibly damage to the structure. Measuring all currently proposed stop criteria requires a lot of single sensors, and it is holding back the application of the method in practice. A method to improve the applicability of proof load testing in practice is to simplify the sensor setup by using semi continuous fiber optical sensors. In this research, a fiber optical measurement system is developed to measure stop criteria for proof load testing. For application of the system to the slab a stiff glued anchorage system is designed, which is able to transfer the concrete strains to the reusable, long gauge fiber optic sensor system. The performance of this anchorage system is checked by preliminary experiments that include the comparison of various glues and investigates time-dependent effects. An installation method for these anchors is developed to improve practical applicability. Stop criteria are developed and checked by a 1:2 scale experiment on a concrete slab bridge. The results from the optical fiber sensors measure the global behavior of the slab bridge such as cracking and deflection well. Internal optical fiber sensors were included within the reinforcement. These sensors provide information on the strains up until the yielding point of the reinforcement bars. In conclusion, the fiber optical measurement system does provide insight on global and local behavior of the slab, and is able to monitor stop criteria. More importantly, the critical location cannot be missed with this sensor setup, as well as that more simplifications in the measurement setup are possible. It is therefore recommended to apply this measurement technique in future applications of proof load testing.

Wind turbines are typically designed for an operational life of 20-25 years. The operation of the assets can be extended beyond their design life if structural components have sufficient reserves left. One approach is to monitor fatigue loads and compare these with design assumptions to determine the remaining useful lifetime of the assets. A major challenge is that sensors for measuring the stress history, such as strain gauges, only deliver local information. Monitoring of every hot spot is technically and financially not feasible due to cost and access restrictions. In addition, strain gauges only have a limited lifetime when compared to accelerometers. Several response estimation or extrapolation methods have been proposed in literature to tackle this problem. All of them are Kalman filter based methods, with the exception of the Modal Decomposition and Expansion Method. A new Kalman filter based method has been recently proposed in literature called Gaussian Process Latent Force Model. The aim of this work is to assess this new method with respect to existing ones both theoretically and numerically. Theoretically, the Kalman filter based methods always rely on white gaussian noise assumptions for the unknown loads, and the modal decomposition and expansion method disregard measurement imperfections. The new method improves upon these assumptions by providing a flexible stochastic definition for the unknown load, and by taking into account measurement imperfections. The numerical analysis is restricted to a comparison with respect to the modal decomposition and expansion method given the different theoretical backgrounds. The comparison is realised upon a simulation which illustrates and validates the methods, and upon a real-life onshore wind turbine equipped with accelerometers and strain gauges. Measured strains are compared with estimated strains. The accuracy of each method is quantified using the mean absolute error and by the correlation between measurement and estimation. The higher accuracy obtained shows that the new method is an improvement upon existing methods. This is further extended in the calculation of Damage Equivalent Loads. The result shows a relative error that, depending on operational conditions, ranges within 20-40[%] for the modal decomposition and expansion method and less than 10[%] for the new method. These results show that the novel Gaussian Process Latent Force Model method should be taken into account for response estimation when accuracy is relevant. Future works should aim on developing a mechanical model that better capture the real behaviour of the wind turbine, as the accuracy of the response estimation methods is mainly controlled by the validity of the underlying assumptions of the mechanical model. Furthermore, the strain estimations should be sought in the whole frequency range, this can be realised by including measurements that deliver this information: GPS sensors or inclinometers for example.

Vibratory installation of sheet piles is the most economic and suitable for the sandy soil because of its mechanism. However, the process induces excess pore pressure in saturated conditions leading to subsidence. This research focuses on the development of a tool-based solution to predict the excess pore pressure due to vibratory installation, which shall be verified by the postdiction of Kademuur Damrak measurements. This tool will help engineers quantify the impact of generated excess pore pressure on adjacent structures. The proposed work attempted to achieve its objective by answering the following main and sub research questions. How can the existing knowledge on generation and evolution of porewater pressure during vibratory installation be effectively integrated into a model/tool, which verified by the postdiction of Kademuur Damrak measurements, can practically estimate the porewater over-pressure during vibratory installations? • What are the parameters that influence the dissipation of the excess pore pressure? • How does the generation and dissipation of excess pore pressure vary in the liquefied zone from non-liquefied? • How can the vibration attenuation affect the generation of excess pore pressure in the sand? • What are the limitations and controlling parameters of this model or tool? The proposed work combines dynamic soil response and transient groundwater flow model to simulate the evolution of pore pressure due to vibratory loading. Based on the degree of modulus degradation due to the vibratory loading the soil, it is zoned into three. This is also termed as a multiscale computational framework. This allows for the formations liquefied and non-liquefied zone. The threshold acceleration of 0.1g - 0.3g must be available for the soil to liquefy [66]. The non- liquefied zone is fed by the groundwater flow from the liquefied zone. According to the Theis equation, head response due to constant pumping in an aquifer is influenced by the rate of discharge (V), storativity(S) and transmissivity(T), distance from the source. This inspired to model the driving of sheet pile with an analogous to the pumping of well. During the constant head loading due to the pumping, there is an increase in the head radially outward from the source. This resembles the phenomenon of pre-shearing in the field. The driving of the sheet pile was stimulated by the constant head loading. The application of time series analysis (PRIFICT method) [8] provides the first estimation of the pore pressure response from the first of three days of field piezometric data of Kademmur damrak. The semi-empirical model was formulated influenced by the head response of the slug test. The response of the time series analysis helped to calibrate the semi-empirical model. The relationship evolved between the physical and modelling parameters established that the hydraulic conductivity is the key parameter in both the generation and the dissipation of the excess pore pressure. The analysis of the field data establishes the conservative assumption of 1m for the width of the liquefaction zone. The analysis of accelerometer data from the Amsterdam noord zuid metro line tunnel helps to establish the dependency of liquefaction on acceleration amplitude. This bolstered the threshold acceleration for liquefaction to be 0.1 - 0.3g. The simple flow only Plaxis model helped to validate the proposed hypothesis. The hydraulic conductivity was established as the key parameter of the model. The integration of dynamic generation and transient groundwater flow model helped to analyse the evolution of excess pore pressure.