Due to the increasing size of offshore wind turbines, the jack-up vessels and cranes installing them need to increase in size. The increase in length of the crane boom results in an increase of the dynamic response of the crane. This increase in response results in higher loads in the crane. The magnitude of the higher loads are unknown and not yet accounted for in crane design. To get a better insight in the dynamic response of the crane and higher loads, a probabilistic model is developed to quantify the annual probability of the crane. To define the different crane operations, a measurement data set of crane operations is made available. From this data set, the different lifts types, exposure times and occurrences are identified. Multiple jack-up configurations are chosen with different water depth and wave heading. Next to the crane and jack-up configuration, long-term wave statistics are used for the environmental conditions, as input for the probabilistic model. Time-domain simulations are performed in a rigid multi-body analysis software, to simulate the dynamic response of the crane, due to wave induced excitations. Due to the random nature of waves, convergence of the results is researched. For this, numerous time-domain simulations with different sea-states have to be performed, this is unwanted. A methodology is developed, using the frequency domain, to limit the number of required time-domain simulations. Using the devised methodology, configurations with similar statistical description can be identified and grouped. The annual probability of failure can be calculated using the devised methodology and probabilistic model. The resulting annual probability of failure is small, much smaller than required. A sensitivity analysis is performed, it is found that the annual probability of failure depends greatly on the input of the probabilistic model. From the results and sensitivity analysis, multiple recommendations are given to decrease the sensitivity of the result. It can be said that from the devised methodology, a basis has been laid to quantify the annual probability of failure of the crane in normal operations.

One of the main drawbacks of offshore wind energy is its high levelized cost of electricity (LCOE). One explanation for these high costs arises from the large amount of uncertainties that come with such complex projects. As a result of this complexity, it’s difficult to foresee the course of events in advance. Decisions made during the preliminary design phase are often based on limited data while having a great impact on project outcome. A tool is needed to assist engineers during these early stages of design. This tool should deliver good results using limited input data and require little computational time. An example of such a tool is TeamPlay; an engineering model that evaluates offshore wind farm design using automated optimization techniques. In its current state, only a monopile type foundation is analyzed within this process. Since the current trend in the industry is to build larger farms further from shore, this foundation type will become less popular in the future. A jacket type foundation is better suited for far-­‐shore application. In this thesis, a new stand-­‐alone engineering model is created which can be added to TeamPlay’s functionality to extend future use...

Due to increasingly efficient engineering of the last decades, the design process for civil structures is heading in the direction of optimal solutions. Among other things, the reduction in self-weight enhances the susceptibility to dynamic loads. As the process for the determination of realistic modal parameters from a digital model often diverges from the reality, the uncertainties should be investigated. This research aims to gain knowledge on the accuracy and stability of such modal parameter estimates. A case study is investigated to determine modal parameters for a realized tied-arch railway bridge. The desired modal properties include the natural frequencies, mode shapes and damping parameters. The frequency domain decomposition (FDD) method is applied on a set of accelerational measurements to determine the modal parameters. Four dominant frequencies are identified and investigated, from which the most dominant operational deflection shapes are extracted. The overall response of the structure shows great similarities with the expected mode shapes for similar bridge types. The computed modes show signs of complex behaviour, due to the characteristics of the load or the structure. An effective method to reduce uncorrelated noise is the application of an autocorrelation function (ACF) to the time domain signals, before execution of the FDD. Prior to the computation of the final results related to the case study, a simplified test case is considered to validate correctness of the FDD implementation.

In order to assist project developers in efficient offshore wind farm installation planning a decision-support tool has been developed. The tool combines Discrete Event Simulation and Black-box Optimization. The focus of the development was on providing the flexibility allowing a user to compare various installation strategies and impact on the total installation cost and vessel workability. Synthetic weather generation with Markov Chains was implemented in order to account for weather effects. Recomendations have been given for weather modelling (for the purpose of installation planning) and for applying different families of black-box optimization algorithms.

As buildings get taller and lighter, structural engineers are increasingly faced with the consequences of the dynamic response of high-rise buildings to wind. Damping is an important property for the dynamic response of high-rise structures, but is a combination of many mechanisms, which makes it a complex phenomenon to account for in the structural design. Empirical damping predictors currently exist, but a large scatter is found among predictors, as well as between predictors and identified damping from measurements. Damping values are prescribed in codes, but are not consistently conservative. Therefore, there is a strong desire from both structural engineers and researchers to obtain further understanding of damping behaviour in high-rise structures. Damping identification techniques, such as the Half-power Bandwidth method and the Random Decrement technique are commonly used. However, these are not applicable to buildings with closely spaced modes, and require extensive measurements. Besides, they only provide a damping value, and cannot find damping of separate components of a system. A novel technique, the Energy Flux Analysis, approaches damping from an energy point of view, making it more widely applicable, and allowing for damping identification in components of a structure. The Energy Flux Analysis has been verified to lab structures, but its performance when applied to a high-rise structures using in situ measurements is still unknown. The aim of this research is to investigate the sensitivities of and prerequisites for the application of the Energy Flux Analysis to high-rise buildings excited by wind using spatially limited measurements. The sensitivities were sought for in the uncertainty of required input for the Energy Flux Analysis: structure motion, which includes internal forces, wind load, data acquisition, and structural properties. The research was performed through application of the Energy Flux Analysis to the New Orleans tower in Rotterdam. While the sensitivity to structural properties and the magnitude of measurements is limited, the Energy Flux Analysis demonstrated to be highly sensitive to the phase of structural motion, internal forces, and wind load. The first two points are relevant for computing the energy flux at the boundary of a system, when one is interested in damping in the superstructure and due to soil-structure interaction separately. The last point is relevant when one is interested in the total or superstructure damping. The phase differences occurring between structure motion and internal forces are a direct result of damping. Material damping resulted in a phase difference between stress and strain in the numerical model, while a local damper resulted in a phase difference between structure motion at different locations. The many damping mechanisms occurring in a high-rise structure may each affect the phase of structure motion and internal forces differently. When these phase differences are not taken into account in the Energy Flux Analysis, for instance due to extrapolation of measurements, an erroneous result will be obtained. A brief investigation was performed as to whether these effects can be expected in true structures, but additional research is required. The fluctuating wind load at the natural frequency of the structure is dominant for the flux of energy from wind to the structure, which is obtained by multiplication of the wind load with the structure velocity. Again, the phase of this wind load is highly important. When measured at one location, little is known about the phase of the wind load at other heights. Different approaches of extrapolating the measured wind load demonstrated a large scatter in the Energy Flux Analysis results. In this research, the Energy Flux Analysis found not to be repeatable, which was proven to be a direct result of the phase difference between the measured wind load and structure velocity. Possible causes for this varying phase difference were formulated. It is essential, but due to the major advantages of the Energy Flux Analysis also profitable, to perform further research into its application to high-rise structures. Therefore, this study provides extensive recommendations mostly focused on simple numerical and lab experiments.

The Haringvlietbrug, which was delivered in 1964 and connects the island of Goeree-Overflakkee to the Dutch mainland, approaches the end of its lifetime. Rijkswaterstaat and Witteveen+Bos investigate the potential use of vibration-based structural health monitoring (SHM) to track deterioration. In vibration-based SHM, a change in the structure’s eigenfrequencies indicates probable damage but most current approaches focus on fundamental modes of the structure which are not or hardly affected by small structural impairments. In this study, vibration-based SHM was employed on local modes of vibration of the Haringvlietbrug, with the hope of detecting small-scale damage. New obstacles arose as a result of this: the influence of environmental and operational variabilities (EOV) on the eigenfrequencies of the structure was often larger than the influence of small damage. As a result, the EOVs should be filtered out of the recorded acceleration data before damage sensitive features can be extracted. To investigate this, 32 accelerometers and 16 temperature sensors were installed in two segments of the Haringvlietbrug. The sensors yield a large data set which was analysed extensively. This made it clear, among other things, that the dynamic response of the bridge deck showed large variability when comparing different vehicle passages. The first research objective was to investigate the applicability of similarity filtering (SF) to filter out operational variabilities from the Haringvlietbrug acceleration signals to extract damage-sensitive features. SF amplified similarities and damped differences between samples of vehicle passages. This way, only consequently excited modes should remain in the signals which were subsequently used as damage-sensitive features for SHM. This study found that using SF to filter the operational variabilities from the Haringvlietbrug data set was ineffective. Three reasons were identified for this. First, the method was not robust as a single deviating sample or a poorly chosen filter coefficient significantly influenced the results. Secondly, missing closely-spaced modes might have caused inconsistency of the results. Lastly, SF was not able to converge to consistent behaviour as the variability of response of the bridge deck might be too great for different passages. The latter reason was investigated further in the remaining of the research. The second research objective was to improve the understanding of the effect of vehicles on local bridge vibrations of the Haringvlietbrug for the application of vibration-based SHM. First, the eigensystem realisation algorithm (ERA) was used to identify and compare consistent mode shapes in order to better understand bridge deck vibrations and find patterns in the eigenfrequencies. ERA is an operational modal analysis (OMA) and output-only system identification technique. ERA was able to identify two consistent modes in the data but they had a large variance in both shape and eigenfrequency. The uncertainties of the results were too large to draw firm conclusions based on ERA because two of ERA’s key assumptions were not perfectly met and the input samples were not optimal. Next, a semi-analytical model of a segment of the Haringvlietbrug was built to simulate the important sources of the variability as found in the acceleration recordings of the Haringvlietbrug. The goal of the model was to investigate the sensitivity of the response of the beam to variations of input parameters. The model was both used for time-history analyses and eigenfrequency analyses. Parametric studies showed that the response of the beam was highly sensitive to the time delay between moving masses (dependent on the axle configuration and vehicle velocity), the unevenness (describing any source of vibration of the interaction between vehicle and bridge deck) and the vehicle velocity. These parameters influenced both the amplitude and the shape of the acceleration response of the Haringvlietbrug bridge deck in the time and frequency domain. The effect of the three dominant parameters was further investigated by simulating four characteristic cases from the Haringvlietbrug data set. Hypotheses on the source of the measurement variability were formed by qualitatively comparing the results of the model to the measured response of the bridge. The model parameters were able to describe a large part of the variability but it was concluded that it is likely that some factors that were not included in the model also play a role in the variability of the measurements. The above findings led to the following recommendations for further research. Firstly, it is recommended to only select similar vehicle passages for SF because this would improve the ability to converge to consistent modal behaviour. Secondly, should someone want to further explore ERA, it is recommended to equip a section of the bridge with a high spatial density of accelerometers to improve the reliability of the results. Next, some recommendations related to the model were made. The first step of follow-up research would be to verify the influence of the model parameters on the dynamic response of the Haringvlietbrug by experimenting with different test vehicles. Subsequently, depending on the importance of the parameters, possible extensions of the measurement set-up for a new campaign were proposed. Secondly, the current model could be improved by introducing more parameters, like acceleration of the moving mass or by implementing a more realistic vehicle model, to be better able to explain the dynamic variability. Thirdly, more parameters could be investigated by building a 3D finite element model. This would make it possible to investigate the influence of the presence of multiple vehicles on the bridge and 3D wave propagation. Upcoming research into data-driven approaches of vibration-based SHM of bridge decks is recommended to focus on similar passages as not all samples contain the same information on the dynamic behaviour of the structure. Decreasing the variability of the input samples might improve the performance of the algorithms.

This thesis aims to investigate the feasibility of developing a successful unsupervised Structural Health Monitoring (SHM) methodology to detect damage in structures, specifically bridges. Detecting damage, especially in its earliest stages, is challenging, thus prompting the need for robust and effective methods. The success of such a methodology could lead to timely inspections and interventions, resulting in significant economic benefits and preventing further damage, including potential failure of the structure in use. The approach involves a literature review to establish relevant background knowledge and useful concepts. From this, a methodology is developed utilizing unsupervised machine learning, specifically Gaussian Mixture Models (GMM), to identify abnormal behavior indicative of structural damage. A Finite Element Method (FEM) model of a simple bridge is created and monitored over a three-year period, serving as a testing ground for the methodology and a primary source for data generation. Temperature data and its effects on the natural frequencies of the bridge model are used to establish a baseline for normal or healthy behavior. Synthetic damage, such as settlement and stiffness reduction, is then introduced to the model to create anomalies or abnormal behavior. The developed methodology is tested using three case studies, each with varying types of synthetic damage. By using both the healthy and unhealthy data generated from the model, the healthy behavior of the bridge is captured using GMM. The model then progressively incorporates unhealthy data into the proposed anomaly detection algorithm. The algorithm evaluates the likelihood of each incoming data point of belonging within the healthy distribution, resulting in data points being classified as either healthy or flagged as abnormal. The case studies presented in this research underscore the efficacy of the proposed anomaly detection approach. In scenarios involving sudden or abrupt damage, the algorithm swiftly and accurately labels abnormal points. For gradual damage scenarios, such as settlement, the algorithm consistently identifies abnormal points, with the rate of abnormal point detection accelerating over time. This detection rate is contrasted with the rate of erroneous abnormal point labeling when processing an exclusively healthy data set through the anomaly detection algorithm. This comparison reveals a higher rate of abnormal point identification when actual damage is present, affirming the effectiveness of the unsupervised SHM methodology in pinpointing abnormal behavior within the modeled bridge structure.

Structural joints influence the design strength, material requirement of a structure. Structural joints experience damping dissipation due to friction damping or hysteresis damping. Damping is often used for reducing the vibrations in a structure. However, large amount of energy dissipation leads to deterioration of the material used for constructing the joint. Hence it is important to identify the system parameters like stiffness, viscous damping, friction force as well as the hysteretic restoring force that cause the energy dissipation in the structure. For identifying the uncertain system parameters like stiffness, viscous damping and magnitude of friction force, the SINDy algorithm is extended by using stick and slip temporal constraints. This is done by segregating the data of external forcing and response of SDoF system, applying the existing SINDy algorithm and applying the sticking and slipping conditions in the time domain. The proposed Extended SINDy approach estimates the system parameters more accurately compared to the existing SINDy algorithm. For studying the hysteresis in the structural joints, a pinned column base-plate was considered in an elastic region. Further, the Dahl model with different slope parameter for each branch of moment-rotation hysteresis is employed. The correct values of parameters are estimated using the Bayesian Optimization technique. This procedure yields a functional form representing a resisting hysteretic moment-rotation behaviour in a structural joint with good accuracy.

Under urban sprawl the trend of new established complex structures has rapidly increased. In this context little importance has been given to maintenance, even if this represents an important step in combating and avoiding disasters and developing improved future structural designs. Over the last years low-cost Global Navigation Satellite System (GNSS) equipment has faced rapid and important development opening a new door to reliable and high accuracy positioning applications such as structural health monitoring (SHM). This study focuses on assessing, from a geodetic perspective, the capabilities of a pair of low-cost dual frequency GNSS receivers for capturing the kinematic response of structures to wind. An experiment has been carried out with a stainless steel cantilever beam, aiming to highlight the advantages of employing a differential GNSS system for monitoring low frequency changes in the structure’s body. Hence, in this context the nominal precision of the GNSS system in East, North and Up direction of 4, 5 and 10 millimeter (1σ), was further improved to 3, 4 and 8 miilimeters in the presence of a Global Positioning System (GPS) based multipath (MP) correction. However, it is safer to consider that the true displacement retention potential of the low-cost GNSS receivers corresponds to 3 times (3σ) the aforementioned standard deviation values, resulting in slightly larger than 1 centimeter detectable horizontal displacements, and up to 2.4 centimeters vertical displacements. To support this, wind-induced beam displacements of up to 1.9 centimeters were identified and attested based on a cross correlation analysis with meteorological information. Next, the architecture of a GNSS based SHM system is proposed that can detect structural displacements in real time and rise safety alarms. Therefore, with real time kinematic (RTK) differential positioning and a position outlier and slip statistical testing procedure, a clear strategy for the estimation and identification of uni- or tri-dimensional displacement quantities in real time is proposed, to rise alarms about the magnitude and the direction of identified displacements. Hence, there is no doubt that newly released low-cost dual frequency GNSS receivers represent an alternative to high-end geodetic equipment for SHM, by offering an optimal balance between precision and cost efficiency.

Over the last decade, the demand for offshore power cables has increased significantly due to, in particularly, the emergence of offshore wind. Despite this growth, industry-wide rules and guidelines to analyse fatigue during cable installation operations are yet to be developed, even though fatigue can become an important parameter in the installation design.  In similar offshore operations, e.g. installation of pipelines or umbilicals, fatigue is assessed based on fundamental fatigue theory like rainflow counting, S-N curves and Miner's rule and therefore these principles form the basis of analysis in this research. However, submarine power cables consist of numerous layers of different materials that result in complex cable cross-sections. The fatigue behaviour of the cross-sectional elements due to external loads has been investigated to determine a maximum stand-by time during installation operations. In this regard, a cross-sectional analysis was performed to establish the stress-strain response of the individual cable components to the global cable deformations. For bending behaviour, the analysis was shown to be consistent with existing test data. However, this type of data is scarce and the analysis was based on a limited sample size. Moreover, geometrical cross-sectional data of submarine cables is rarely provided by suppliers and hence several assumptions were made in the analysis. For future work, it is recommended to develop in-house test data of cables such that the cross-sectional model can be accurately verified and improved. The cross-sectional stresses were implemented in a fatigue assessment model, in which the local stresses were calculated based on the output of global modelling software OrcaFlex and subsequently analysed with rainflow counting and Miner's rule. The model was applied to three cable types and it was found hat lead, used for cable sheaths, is the critical cable component in terms of fatigue. When no lead is used in the power cable, the conductors are the critical component. Furthermore, for mild loading scenarios, i.e. waves with significant wave heights Hs≤1.5 m, the model yields components infinite life for all cable components. For higher load cases, i.e. Hs≥2.5m, the model showed that fatigue mitigation measures are required to stay within the fatigue budget. Several mitigation methods were implemented, from which it was found that increasing the layback length of the cable combined with adjusting the vessel heading to favourable conditions most effectively reduces fatigue damage as all load cases with Hs=2.5 m resulted in maximum stand-by times of tsb≥ 125days. However, for higher loading scenarios, i.e. Hs=4 m, all researched mitigation measures were insufficient to stay within fatigue budget. For future work, it is therefore recommended to improve the mitigation measures such that severe load cases can be survived. Lastly, the wave conditions were simulated as JONSWAP waves. These simulations are time-consuming and therefore not many loading scenarios were modelled. It is recommended to research methods to approximate wave conditions with regular waves, as the duration of OrcaFlex simulations will decrease exponentially. An increase in number of load cases will result in more accurate limit states for the cable components.  

The offshore wind energy industry is quite literally reaching for the sky to meet the increasing demand for renewable energy in Europe. Offshore wind turbines are sprouting out of the waves off the coast of many European nations and their size is increasing rapidly. As the turbines become larger and the waters in which they are placed become deeper, the foundations that carry these behemoths must grow as well. The most frequently used foundation is the monopile foundation. Over the years, both the length and the diameter of these monopiles have steadily increased. Installing monopile foundations has, as a result of this, become more and more difficult. An important aspect of the installation of monopile foundations that has become more challenging is the upending of the monopile. When the monopiles are transported to the site of installation, they are sea-fastened to the deck in horizontal orientation. When they arrive, they need to be rotated to a vertical orientation for installation. This can be done with the aid of an upend hinge. This thesis focusses on the modelling of such a hinge for operability studies. The goal of these operability studies is to determine the weather and wave conditions in which the operation can be safely performed. Conventionally, the stiffness of the upend hinge is approximated by several linear springs between the vessel and the monopile. The aim of this thesis is to apply dynamic substructuring to existing Finite Element models of a hinge to more accurately describe its dynamic behaviour within hydrodynamic simulations. Hereafter, the response of the dynamically substructured model can be compared to the response of the conventional model. The findings of the research show that it is possible to use dynamic substructuring to reduce the Finite Element models of an upend hinge and implement them into hydrodynamic simulations of an upend operation. When comparing the conventional model to the substructured model, it can be seen that there is a significant difference between the high-frequency response of the models. However, the responses of the models to prevailing ocean waves are very similar. For operability studies, this response is most relevant. Furthermore, the required computation time is significantly higher for the simulations employing the dynamically substructured models than for the conventional models. Therefore, it is not recommended to apply dynamic substructuring to model this upend hinge in operability studies. For situations where the high-frequency response is more relevant, however, dynamic substructuring may prove a valuable tool to more accurately describe the dynamic properties of an upend hinge or other marine equipment.

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.

The dynamic behavior of structural components can largely change in the presence of damages. Understanding this behavior is of particular importance for critical engineering systems, and in particular bridges. Damage identification methods forms a key objective in structural health monitoring of bridges so many researches have been conducted in this area. In this thesis, damage identification techniques on beam bridges under moving vehicle loads will be presented in order to produce useful conclusions about the assessment of existing bridges by investigating various numerical applications of different scenarios. The first objective of this thesis is to derive the analytical expressions needed to be able to predict the dynamic response of many different cases of bridges so that as many real scenarios as possible can be treated. This means that these expressions would be used to investigate damaged beam bridges that can be modelled as an assembly of beams with any number of different material properties, any type of interface or boundary conditions and any number of cracks. For this reason an approach to analyze the bridge as an assembly of n piecewise homogeneous damaged Euler-Bernoulli beams jointed at their edges, will be presented, using the generalized functions to obtain a single expression of the solution which depends on the 4 integration constants associated with the boundary conditions. The closed-form expressions of these 4 constants will be provided. Furthermore, in the presence of internal or externals springs, translational or rotational, additional constants representing the discontinuities have to be taken into account and are computed by considering one additional condition for each discontinuity. The feasibility of this approach and the corresponding analytical formulations is shown with two numerical applications that include all the different capabilities mentioned. Moreover, the implementation of these expressions in a deterministic approach for damage localization is presented, mainly as another example of the many possibilities of the use of analytical formulations instead of other approaches and as an introduction of the so called Inverse Problem with deterministic and probabilistic methods. The second objective concerns the optimization of damage identification on bridges by comparing different quantities that are evaluated while measuring the response of the bridge (direct monitoring) and the response of the moving vehicle when it passes along the bridge (indirect monitoring). First, the governing equations for the dynamic response of these models are derived, considering the crack(s) as a rotational spring, the bridge as an Euler-Bernoulli beam (or multiple with different properties) and the moving vehicle as a spring-mass system. In this manner, the dynamic response of the bridge is calculated (modal characteristics and displacement) as well as the one of the moving oscillator (displacement and acceleration) and the reaction force acting on the surface of the beam from the moving vehicles. Numerical applications with different beam properties and different number of cracks are performed, using MATLAB for the analytical expressions and SAP2000 for the finite element model, to derive the optimal quantity to be used for damage identification. Lastly, the results are validated by considering and comparing an alternative way of modelling crack, namely as a zone with reduced rigidity, for the same numerical examples, leading to the same conclusions about the crack(s) identification. Last but not least, the third objective of this thesis is to be able deal not only with the widely used time-invariant damages, namely the always-open crack model, but also with time-variant damages and in this case with the switching crack model. To achieve this, the analytical expressions for the closed-form solutions of the mode shapes derived for the always-open crack are modified to be able to tackle the switching crack model by introducing a Boolean switching crack array which identifies open cracks, modelled as rotational springs. These new expressions would still be able to be used for any number of Euler-Bernoulli beams, any type of interface or boundary conditions and any number of switching cracks. Then, the governing equations for the dynamic response of this model are derived, considering the moving vehicles as moving masses in order to validate the approach with numerical examples existing in the literature and then by introducing its new capabilities. Further, as the computational strategy has been validated, a comparison between time-variant and time-invariant damages is performed concerning crack identification, so that the reader would recognize the importance of understanding the dynamic behavior of different ways of modelling damage in complicate engineering systems like bridges.

Wind power to cold climate sites is attractive because of favorable wind conditions and low population density. However, icing of wind turbine blades remains one of the main challenges for cold climate sites. Ice formation on wind turbine blades causes several problems, such as the loss of power production, unbalanced loads in drive train and ice throw. Most of the existing detection methods need special sensors installed and would be expensive to achieve a satisfied accuracy. In this research, an alternative model-based ice on blade detection method is proposed. There are mainly two advantages of the method. Firstly there are no additional measurements are needed. Secondly the detection method can be implemented for any kind of blade aerodynamic changes, not only ice on blade. The basis of the mode-based ice-detection method is a reliable linearized wind turbine model. The accuracy of the model is mainly influenced by the number of degrees of freedom (DOFs) and the number of operation points (OP). The most efficient model is the one with less DOFs and OP but without losing much accuracy. The relative importance of DOF and OP is revealed and suggestions are given for an optimum choice of DOF and OP for different quantities. Specifically, for power production properties, increase the number of OP is more efficient. While, for blade and tower related properties, increase the number of DOF is a better choice. The ice on blade influence of aerodynamic force, power production and structure loads is studied for three ice conditions, which are start ice, light ice and moderate ice. Based on the ice on blade influence and the reliable linearized wind turbine model, the concept of model-based ice-detection is proposed as follows: Firstly ice on blade changes the aerodynamics and the blade mass. Therefore it can be considered as one wind turbine system changed to a different system. That means the outputs of the two systems, with ice and without ice, are different, even the inputs are exactly the same. From the ice-detection point of view, if the difference of outputs between iced system and cleaned one is much larger than modeling error, ice on blade can be detected. With the proposed model-based detection method, the ice-detection capability of different output quantities is studied. It has been found that the power production related quantities usually have the highest ice-detection capability, while the blade and tower properties have relatively lower capability. In order to verify the ice-detection method for a broad working conditions, implementations are performed for above and below rated wind speed conditions. Successful detections have been achieved for both of them. For possible measurement errors in reality, they are efficiently considered by introducing uncertainty factors. The ice-detection capabilities with measurement error have been analyzed for three ice conditions and detected based on different quantities. Although a drop in the detection capability has been observed if considering the measurement error, all the quantities still can detect the ice successfully to some degree.

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.