Traffic congestion is an ever-growing problem in large cities, with significant implications for social and environmental well-being. For instance, the average New Yorker travelling during peak times sits in traffic for 60 hours longer than in 1987, culminating in increased stress and exposure to pollution. While New York and other large Western cities can combat this problem through a combination of improved public transport and pedestrianisation, these solutions are notably more difficult to implement in postmodern cities with harsh climates. A prime example of this is Dubai; its unorthodox urban planning and limited public transport infrastructure leave road expansion as the primary economically viable remedy to congestion. This underscores the urgent need for an innovative transportation solution...

Carbon Fiber Reinforced Polymers (CFRPs) are increasingly employed in aerospace applications due to their high strength-to-weight ratio and contribution to fuel efficiency and reducing emissions. Yet, their vulnerability to complex damage modes, particularly impact-induced delamination, presents critical challenges for structural integrity and airworthiness. Delamination often initiates beneath the surface and evolves under cyclic loading in ways that remain difficult to detect and predict. Addressing this challenge, this thesis develops and validates advanced prognostic methodologies for monitoring delamination progression and predicting the remaining useful life (RUL) of CFRP structures under compressive fatigue loading.
A dedicated experimental campaign was designed, beginning with low-velocity impact testing of woven CFRP specimens, followed by compression–compression fatigue. Two active sensing-based structural health monitoring (SHM) techniques—Guided Waves (GW) and Electromechanical Impedance (EMI)—were utilized. Ultrasonic C-scan inspections were used to label delamination growth, enabling a direct correlation between sensor-derived information and physical damage evolution.
Building on this foundation, the thesis first examines the diagnostic capabilities of GW- and EMI-based indicators through advanced signal processing, establishing their sensitivity to progressive delamination and their relevance for life prediction. The research then explores how integrating the two sensing modalities enhances prognostic accuracy, showing that their complementary nature improves robustness across varying impact severities and fatigue regimes. A further line of investigation focuses on GW-based frameworks that explicitly link delamination size to fatigue life. One framework analyzes the sensitivity of individual sensor–actuator paths, providing spatial insight into delamination growth, while the other develops specimen-level correlations that generalize across different configurations. Finally, a multi-level deep learning approach is introduced, where raw GW signals, transformed components, and engineered features are processed in parallel. Embedding domain knowledge into the model architecture improves predictive accuracy, generalization under diverse loading conditions, and explainability of delamination-driven failure mechanisms.
Overall, this work demonstrates that active sensing techniques with advanced AI-driven models substantially enhance the ability to monitor and predict degradation in composite structures. The contributions provide both methodological advances in SHM and practical pathways toward safer, more efficient, and sustainable aerospace operations by mitigating the hidden risks of delamination-induced failure.

This dissertation introduces a comprehensive end-to-end framework related to Prognostics and Health Management (PHM) strategy, with the goal of utilizing raw sensor (placed on a structure's components) data to make maintenance decisions for extending the structure's lifecycle. The study addresses key challenges in PHM, such as dealing with high-dimensional, multi-modal data, extracting features, predicting Remaining Useful Life (RUL), managing uncertainty, modeling repair scenarios, and optimizing maintenance decisions. All these challenges are organized in phases. These phases are namely, i) data collection, ii) feature extraction, iii) Health Indicator (HI) construction, iv) prognostics, v) modeling of maintenance actions, and vi) Post-Prognosis Decision-Making (PPDM). Each phase of the PHM strategy is developed independently but integrated into a cohesive framework, ensuring modularity, transparency, and adaptability across diverse applications.

A major contribution to the framework is proving that neural networks, a preferable approach for handling complex data, can become interpretable, thus unveiling the black-box nature of such models. In this regard, the ISTRUST model, an interpretable Transformer-based architecture for predicting RUL directly from raw sequential image data of a structure under fatigue loads, is proposed. By leveraging attention mechanisms, the model captures critical spatiotemporal features of structural damage, offering insights into prediction accuracy and variability. The model's interpretability highlights the understanding of its predictions. Simultaneously, via this interpretation, it is shown that predicting RUL directly from high-dimensional raw data is challenging or even impossible, necessitating focusing on each phase of the PHM strategy separately instead of unifying the majority of the PHM strategy in one model.

Central to the framework is the development of the Deep Soft Monotonic Clustering (DSMC) model, designed to extract meaningful features and then construct HIs from multi-modal data. This model extracts monotonic features that are related to each component's degradation. Subsequently, it considers these features to perform monotonic clustering representing HIs and enables the integration of those HIs into prognostic models to predict RUL across diverse domains.

The dissertation further explores the impact of imperfect repairs on components, where repairs often leave the component in a state between fully restored and partially damaged. The health state of the component is measured via a stochastic recovery of the predicted RUL and a Bayesian inference-based model is employed to quantify this stochastic behavior. The imperfect repair (Bayesian) model can be also extended for multiple sequential repairs. This phase of the PHM strategy emphasizes the importance of understanding imperfect repair effectiveness to enhance maintenance strategies.

The final phase of the framework concerns PPDM. Given a set of maintenance actions, including replacements and imperfect repairs, and a set of operational conditions and constraints, PPDM is modeled as a Markov Decision Process and optimized via deep Reinforcement Learning. PPDM's ultimate target is to optimize the scheduling of maintenance actions proactively within a predefined horizon length.

Experimental validation is conducted on each of the proposed models. The ISTRUST model was validated on fatigue-loaded composite specimens, utilizing sequential raw image data taken by a camera. The DSMC model was tested using diverse datasets, including engineering and healthcare. The imperfect repair modeling was applied to tension-tension fatigue experiments on open-hole aluminum coupons, capturing stochastic recovery behavior after repairs. The same experiment was utilized for evaluating the PPDM framework.

Overall, a holistic end-to-end PHM framework lays the groundwork for advancing Condition-based Maintenance (CBM) strategies. By integrating advanced models for each phase of the PHM strategy, the research highlights practical opportunities for embedding PHM into CBM and encourages further innovation and refinement by other researchers in the field. Although the proposed PHM framework has been an initial attempt towards this direction, its capabilities can be further enhanced through improvements in data scalability, exploration of varied PPDM formulations, sensitivity analyses across PHM phases, and practical integration with CBM for broader system-level maintenance optimization.

This thesis presents a Stochastic Finite Element Method (SFEM) framework to improve the reliability analysis of composite materials, accounting for material uncertainties which often arise from manufacturing imperfections. These uncertainties lead to discrepancies between experimental and numerical results, typically addressed through conservative safety factors. The framework integrates the Karhunen-Loève (KL) expansion to model spatial variability in material properties and Latin Hypercube Sampling (LHS) for efficient probabilistic simulations. Two KL expansion methods are compared - the Galerkin and Bounding Box approaches - and the latter is found to be more computationally efficient. A continuum damage model (CDM) simulates progressive failure in composite laminates, and the methodology is validated through test cases such as Open Hole Tension. The results show that incorporating spatial material variability leads to more accurate predictions of failure probabilities, potentially enabling reduced conservatism in the design phase. This work supports the use of stochastic methods for improving reliability-based design of composite structures.

The growing use of composite materials in engineering applications accelerated the demand for computational methods, such as multiscale modeling, to accurately predict their behavior. Combining different materials with target mechanical properties helps achieve optimal structural performance. Nevertheless, the complex nature of composite materials poses several challenges. Current multiscale methods, such as the $\text{FE}^2$ method, are hindered by a computational bottleneck, limiting their widespread industrial adoption. Most of the existing surrogate modeling techniques that address this bottleneck are limited to predicting homogeneous materials or require an extensive dataset. The application of surrogate models in the fracture mechanics field is largely unexplored, where the existing models are highly convoluted.

The goal of this thesis is to apply surrogate modeling to predict cohesive damage in the fracture mechanics field. It focuses on one of the existing techniques using Physically Recurrent Neural Networks (PRNN). The core idea behind PRNNs is to implement the exact material models from the micromodel into the material layer of the network. The PRNN, which incorporates an elastoplastic model in what is referred to as bulk material points has resulted in exceptional performance when predicting elastoplastic behavior in composite materials. The primary objective of this thesis is to extend the existing PRNN to predict the effect of debonding at the fiber-matrix interface while capturing path-dependent behavior and minimizing the size of the training dataset with excellent extrapolation ability.

The fundamental capabilities of the existing PRNN with bulk material points only are evaluated in the microscale cohesive damage framework, particularly when interface elements are implemented at the fiber-matrix interface of the micromodel. This initial step reveals the limitations of the existing architecture and it becomes apparent that all types of nonlinearities present in the micromodel must also be implemented in the network.

This thesis extends the PRNN by incorporating a Cohesive Zone Model (CZM) within the existing material layer. This new architecture introduces cohesive integration points with the CZM along with the bulk integration points. Through model selection, various configurations of bulk and cohesive points are explored, along with different training dataset types and sizes, to maximize predictive accuracy and extrapolation capabilities. It is observed that training with non-monotonic data is required for the network to learn both types of nonlinearities. The limitations of the network's prediction are noted, which are due to the fact that its architecture does not represent the stress homogenization step of the multiscale method. This realization highlights the importance of the layout of the PRNN.

Further study investigates new PRNN architectures to improve the physical representation of the micromodel. The networks are trained on a single curve to select the optimal architecture. The most promising option is discussed in detail, in which the history parameter of the cohesive points is input to the bulk points. The network is proven to provide accurate prediction on a small training dataset when tested on the training dataset. Constraints of the PRNN are discussed and further improvements are recommended to extend the modified PRNN to a larger dataset.

This research contributes to the field of surrogate modeling for composite materials by investigating the predictive capabilities of the PRNNs and exploring new architectures. The results provide a promising outlook for accurately predicting the complex behavior of composite materials, specifically in the context of cohesive microscale damage considering debonding at the fiber-matrix interface. The proposed PRNN has the potential to increase computational efficiency of multiscale modeling in engineering applications.

Composite structures are used more frequently in newly designed aircrafts. This results in new difficulties compared to conventional metals during operational life. Especially, the material properties of two different materials can cause side effects, which are unwanted in a structure, for example thermal stresses. Many reports have been written about the difference in damage inflicted by thermal fatigue stresses compared to mechanical fatigue stresses. However, the environmental conditions and stacking sequences used in these reports are extreme and not common in aviation. This thesis focuses on the effect of combined thermal and mechanical fatigue load on the structural integrity of a laminate, but with realistic operations mechanical and environmental loads.

In-service F-16 data has been used to get a proper indication of the order of magnitude of mechanical and thermal forces applied on an aircraft during operations. These conditions are applied to a semi-quasi isotropic coupons, which were dominant in the 0 direction, to simulate a more common laminate for an aerospace structure. The residual transverse shear strength, residual bending strength, and stiffness are measured after a mechanical and/or thermal fatigue load has been applied to test specimens.

Results show no clear reduction for any of the measured material properties. The main differences in results can be derived from size differences in test coupons. However, all results are within the scattering region of the reference values. Thus, it can be concluded that the structural integrity of a laminate will not be affected negatively by a combined thermal and mechanical fatigue load, if the conditions match the used operational circumstances.

Wind turbines are operating in harsh environmental conditions, especially offshore. An implication of these conditions, caused by the impact of precipitation, is the development of leading edge erosion (LEE). This leads to degraded blade surfaces that result in lower aerodynamic performance. Leading edge erosion is researched in many ways but remote detection is still underdeveloped. Therefore, this thesis investigates the possibility to develop a LEE detection method by analysing real-life data from operational turbines in the field.

The failure of composite laminates is most commonly evaluated through deterministic approaches. However, even though these traditional methods predict the failure well on average, they cannot take into account the inherent probabilistic aspect of failure mechanics. Thus, reliability methods allow for a better structural assessment and improve the efficiency of the design. One of the most used methods, FORM, is shown to be limited and unreliable for applications in composites. Monte Carlo methods are therefore further used in this research. Furthermore, current reliability research in composites uses very simple models of damage and failure. Based on Monte Carlo simulations, this research was able to include a failure mode-dependent criterion and proposes two methods to model reliability in terms of last ply failure rather than first ply failure.

Improvement in the SHM of composite materials requires an enhanced understanding of the damage accumulation processes and helps in the way towards lighter, more optimized, and more sustainable aerospace structures. The Digital Twin concept has the potential to address this problem and may revolutionize the designing, certifying, maintaining, and operating of systems and their components in the long term. This thesis presents a first step to assess its fit in the prediction of damage accumulation within composite materials, specifically the accumulation of transverse matrix cracks within a carbon/epoxy cross-ply specimen under a quasi-static tensile load. To make use of a data-driven technique, a sufficiently large data set is essential. An existing dataset of experiments provides a solid basis but needs augmentation. To augment an existing dataset of tensile tests on cross-plies, a FEM model was constructed that models both transverse matrix cracks and delamination by making use of XFEM-CE. Material variability is implemented per element in the model to overcome the deterministic nature of FEM and generate various crack patterns. The crack patterns and mechanical behavior of the FEM simulations show to be in good agreement with the experimental data. The digital twin that provides real-time predictions is proposed as a LSTM-based neural network. The applied strain to the specimen was predicted with reasonable accuracy. The error of predicting the next crack decreased significantly between predicting the 2nd and 5th crack, after which the mean value of the error and standard deviation remain at similar values. These findings imply that from predicting the 5th crack onward, stochasticity’s role is minimized on the part of predicting the next crack strain. The stochasticity and underlying relationship between the locations of the cracks turned out to be too complex to model with the given approach. Two of the main reasons that are attributed to the latter phenomenon are the modest size of the data set, in spite of further augmentation of the data set after theaddition of the FEM crack patterns, and chosen approach in problem definition.

This research experimentally investigates the effects of an impact event during fatigue loading of a CFRP structure. Open-hole specimens were impacted in-situ during a short interruption of a tension-tension cyclic loading program. The impact was aimed directly below the central hole, with the critical fatigue damage path being left and right of this hole. A combination of digital image correlation (DIC), acoustic emission (AE) and C-scan measurements was used to measure the material response and damage patterns. The results indicate that while an impact caused the total amount of damage to increase as one would expect, its effect on the damage along the critical path depends on the timing of the impact. Only an impact before fatigue clearly accelerated fatigue damage accumulation leading to a shorter fatigue life. In contrast, impacting specimens at a later moment during fatigue loading had no effect on this critical damage accumulation.

The field of prognostics on composites is relatively young, and research is focused on constant amplitude fatigue (CAF) loading, whereas variable amplitude fatigue (VAF) loading is more common in actual use-cases. Therefore in this research, the feasibility of different in-situ, data-driven probabilistic models is studied for prognostics on carbon fibre reinforced polymer (CFRP) specimens under VAF. Fatigue data with recorded acoustic emissions (AEs) is available from an earlier performed experimental campaign. Three models were selected: a statistical model to be used as a baseline comparison, a Gaussian process (GP) regression using cumulative AE energy, and a recurrent neural network (RNN) using all available AE features and load data.
The models were compared for performance of remaining useful life (RUL) predictions on specimen under VAF loading for three different cases; when trained on CAF, VAF, and the combination of these two. Seven performance metrics were used to quantify their performance, as well as a qualitative comparison.
The statistical method's performance varied per test specimen and can, therefore, only be used in practical applications when used very conservatively. It did not perform better in any of the three training cases as compared to the others.
The GP regression was deemed not feasible due to high variability in its RUL predictions, high uncertainty due to the setting of a failure threshold as probability distribution based on other specimens, and high computational costs. The performance differed per test specimen as well.
Finally, the performance of the RNN increased when trained on VAF data as compared to training on solely CAF data. It increased further when including both CAF and VAF in the training data. It is not yet feasible to be used in practice, due to variability in its predictions, and the inability to handle outliers. The latter is an issue for the other two models as well.
The RNN outperformed the other two models when trained on VAF, and CAF and VAF data. Due to the definition of the failure threshold in the GP, the GP did not perform better than the statistical model. In the case of CAF training data, there was not a clear distinction between the performance of the statistical model and the RNN, except for one performance metric related to the precision of predictions.
During this research, AE data from glass fibre reinforced polymer specimens tested on tension-tension fatigue under different load levels became available. In a case study, the feasibility of a RNN, trained on AE data, was analysed for prognostics on this data-set. Due to large differences in the life-times between the specimens in this data-set, this was not feasible. Extending this RNN with a feedforward neural network which uses load data as well as input, provided worse predictions.
The main conclusion drawn in this thesis is that with the current implementation of the used models, in-situ, data-driven prognostics on composite specimens under VAF is not yet feasible.

Estimating the RUL (Remaining Useful Life) of machinery is a useful tool for maintenance and performance operations. This results in lower costs, improved safety and operational improvements.
This paper proposes two adaptations to the CNN-LSTM network provided by Li et al. \cite{Li2019APrediction}, as well as exploring reproducibility, accuracy and sensitivity of the original DAG (Directed Acyclic Graph) network. The network at hand is an ensemble network combining LSTM and CNN neural networks to provide an accurate regression RUL prediction using the NASA CMAPSS dataset \cite{NasaNasaReprository}.
The Adaptable Time Window (ATW) adaptation increases the amount of time cycles that can be predicted and increases the accuracy, allowing for earlier predictions and better RUL predictions. Allowing state-of-the-art predictions accuracy for complex datasets. The Sub-network training adaptions did not surpass the accuracy of the original network with the current implementation settings, however is promising for further research.

A lot of studies have been done on Acoustic Emission (AE) covering a wide range of materials and applications. Within a structure that is under loading, AE has proven to be useful in determining the type, location and accumulation of damage within a material. The general goal is to use AE for inservice monitoring of structurally loaded parts. However, more research is necessary before the method can be employed in real-time applications. The objective of this project is to contribute to the development of algorithms that classify failure modes real time in composites using AE. Composite material specimens are loaded under tension while recording AE, after which the signals that are recorded are used to create an independent damage mode signature. This leads to the ability to classify damage modes per time increment. Mechanical tests are performed on specimens while recording AE signals. Carbon fiber reinforced polymer specimen with unidirectional 90∘layup are loaded in tension up to failure to create a fingerprint of the matrix cracking damage mechanism. The same material with a crossply layup is loaded in tension under quasi static and fatigue loading. The fingerprint is then used to separate signals coming from this type of damage and other damage mechanisms. Next to the basic parameters of these signals,
additional features are generated by processing the waveform of each signal. The wavelet transform is used to find frequency related features that characterize each signal. Machine learning algorithms are developed and used to cluster and classify the AE Signals. Overall, the real-time clustering algorithms have proven to be successful in clustering and classifying incoming AE signals in an efficient way. The system was able to correlate incoming data to matrix cracking data, within a very limited time frame. With room for optimization the proposed methods seem fit to be applied in real-time applications. Before this can be done however, more testing and validation of these methods is required. The most valuable addition to this study would be a proper validation of the clustering results. Regarding improvement of the system, the main bottleneck of the proposed algorithms is loading large datafiles. This can be solved by reading data in batches, but requires further development of the algorithms.

High performance continuous fiber-reinforced composites are becoming increasingly important in the aerospace industry. In these structures, internal damage is often created and propagated throughout the lifetime, having a negative impact on the structural properties. In order to allow for condition-based maintenance, there is a need for reliable prognostics to predict future damage states. Therefore, a model-based machine learning approach is developed in this thesis to perform prognostics of the remaining useful life (RUL) and remaining useful properties (RUP) of cross-ply composites to end-of-early-fatigue-life (EOEFL). In-situ transverse matrix crack density and dynamic stiffness data are available, as well as offline delamination ratio and damage induced stiffness degradation data. To account for the multicausality and non-linearity in stiffness degradation, the evolution of cracks and delaminations is modeled using separate phenomenological relations that are pre-trained with high variance using non-linear least squares (NLS) on a separate training set. These damage properties are then combined into a normalized stiffness prediction using NLS pre-trained phenomenological relations that express the induced stiffness degradation for each of the aforementioned properties. A particle filter (PF) trains the model parameters, initialized in a high variance uniform distribution, for the phenomenological models in real-time (i.e. online). This is done with the in-situ crack density and normalized stiffness measurements of the testing specimen only. A random walk on the model parameters, which declines towards EOEFL with a given rate of convergence, is added to allow for continuous adaptivity. By propagating each particle to future states beyond the online time step in the PF, the prognostics are obtained. This encompasses the RUP and RUL to EOEFL using a weighted sum of the particles. After applying this methodology to the case study data, it is concluded that the potential of PF to offer adaptivity required for RUP prognostics of composites is identified, definitely for damage properties showing early characteristic behavior. However, reliable RUL estimation to EOEFL with the methodology set out in this thesis remains difficult. Especially the stiffness degradation model and the failure criterion for EOEFL generate difficulties. Therefore, a promising recommendation would be to combine similar phenomenological relations to end-of-life for crack and delamination growth with an alternative stiffness model. A feasible approach would be to train a surrogate model on synthetic data generated with finite element modeling simulations. Finally, a sensitivity analysis is done on three PF hyperparameters: sample size, threshold effective sample size, and rate of convergence. The first shows that a minimum sample size can be distinguished after which no improvement occurs when the sample size increases. The second indicates a 'sweet spot' that balances the sample impoverishment and weight degeneracy drawbacks. The latter makes it plausible that a moderate rate of convergence is preferable.

Adhesively bonded joints have proven to outperform their mechanically fastened joint counterparts, as they present a more structurally efficient method of load transfer, lower stress concentrations and better fatigue performance at reduced weight. In the specific case of the Adhesively Bonded Single Lap Joint (ABSLJ), bending-induced stresses that result from the load path eccentricity add up to the adherend inplane stresses. Moreover, significant peak peel and shear stresses develop at the lap ends of the adhesive and associated adherend interlaminar tensile stresses have a detrimental effect on the joint’s strength. Such joints made of Fiber Reinforced Polymer (FRP) adherends bonded with an epoxy adhesive layer sustain a substantial amount of damage, from failure onset to ultimate failure. With the purpose of design structurally efficient and damage tolerant composite joints, it is essential to understand the stress distribution and to accurately predict the damage initiation and propagation events in such joints made of composite materials.

A well-established set of Damage Progression Models (DPMs) in the framework on the Continuum Damage Models (CDMs) were developed as a tool to predict the global response,damage initiation load and ultimate load of the specimens. Hashin 3D, Puck and LaRC05 werethe implemented failure criteria to detect the initiation of damage in the adherends. After thispoint, the elastic properties of the detected damage elements were reduced according to sudden and gradual material degradation models. As for the adhesive, the von Mises criterion was used to detect the damage onset and a linear softening law modeled the material degradation. For the validation of the DPMs, the numerical results were compared against the data of an already published experimental study. Four different adherend layup sequences: [45/90/ − 45/0]2푠, [90/−45/0/45]2푠, [0/45/90/−45]2푠 and [45/0/−45/0]2푠 were studied based on data extracted
from the mechanical testing, Digital Image Correlation (DIC) and Acoustic Emission (AE).

Good correlations between numerical predictions and averaged experimental linear stiffnesses were found, particularly for the two configurations with the outmost ply at 45∘, for which the difference was lower than 5%. The initial non-linear stage of the global response seems to be governed by the longitudinal bending stiffness, while the subsequent linear behavior is controlled by the longitudinal membrane stiffness of the adherends. Regarding damage initiation, numerical predictions showed to be 11.5%, 7.5%, 29.9% and 6.1%, respectively, more conservative for the four analysed configurations, when compared to the AE results, whose established criterion should be further developed. With respect to the ultimate load, the relative differences between predictions and tests showed significant variability among the tested configurations; specifically the deviations were of: 33.2%, 37.4%, -0.4% and -13.71%.

Despite the encouraging results, an inherent shortcoming of CDMs is the representation of damage in a smeared manner due to the homogenization of the anisotropic material in the modeling process. A blended framework using CDMs to model intralaminar failure and discrete crack models to model interlaminar failure and matrix cracking might lead to more realistic damage patterns.

The first launch of the Ariane 6 launch vehicle is planned for 2020, however in its current design no significant part of the launcher will be reusable. A current trend in the global space market is decreasing the costs of spacecraft launches through recovery, retrieval and refurbishment of parts of launchers. As a first step towards this market demand, it is to be investigated whether it is cost-effective to recover, refurbish and reuse the key components of the first stage of the Ariane 6, which are contained in the Vulcain Aft Bay (VuAB). This is where the engine, fuel lines, thrust frame and electronics are attached. The team has the task to develop a cost effective way of reusing the Vulcain Aft Bay. In the preceding report, multiple concepts were analysed and one concept was selected to complete the conceptual design. This concept consists of an Inflatable Heat Shield for re-entry, a Parafoil to control the flight at lower altitudes and a Mid-Air retrieval using a helicopter to perform a soft landing. A functional analysis was performed to define concept specific functions. This was done by means of a Functional Flow Diagram and Functional Breakdown Structure. In order to fulfil these functions, simulations were created of the most critical moments of the mission. One set of simulations analyse the trajectory of the system throughout the mission, predicting the location of landing. The other set of simulations is used to predict the critical load cases of the system. The aforementioned simulations were used to design the individual components of the system. By integrating these simulations and managing the iteration process, the overall system characteristics and configuration were established. The system was then analysed for sustainability, reliability, risk, maintainability and safety. The requirement compliance of the system was then updated, detailing which requirements have achieved full compliance, marginal compliance, no compliance and which have not been sufficiently investigated. Proposals to make all requirements fully compliant are included in a feasibility analysis. These include different design approaches for the team and design changes to the VuAB to accommodate the recovery system. From there strategies on future verification and validation activities was set forth together with operational, refurbishment and production plans. These plans and the design of the system were used to create an updated business model with return on investment figures, which predict a significant cost reduction on a per launch basis within five years. Finally a set of future recommendations and plan is proposed for the continuation of the project.

In general, damage models for Fibre-Reinforced Polymers (FRPs) can be divided into two categories: Continuum Damage Models (CDMs) and Fracture Mechanics Damage Models (FMDMs). Damage models within each of these categories are advantageous for different type of failure mechanisms. Therefore, it is of interest to develop a unified framework in which both categories can be integrated (blended) to achieve a more complete representation of failure mechanisms and their interactions. Furthermore, the unified framework aims for applicability for both quasi-static and fatigue loading. Three damage models have been considered for fibre, matrix and delamination failure. For each damage mechanism it has been demonstrated how the chosen model fits into the unified framework. However, on a short term the feasibility of the framework in a practical implementation is deemed to be unrealistic. The challenges that have to be overcome, before such a framework can effectively be used for practical applications, are explored.

The high specific strength and stiffness of composite materials have resulted in a widespread introduction of composite structures in the commercial aviation industry. Although composite materials offer the possibility to design lightweight structures, they also suffer from complex failure modes of which damage progression is badly understood. This thesis research is about Structural Health Monitoring (SHM) for composite aircraft structures. By permanently attaching a sensor network to a structure, maintenance activities can be improved by reducing costs and by optimizing maintenance scheduling. SHM could furthermore be used to increase the understanding of composite behavior. This research focuses on investigating an image reconstruction algorithm which can be used to visualize impact damage on a composite plate using piezoelectric (PZT) sensors. The relationship between the reconstructed images and damage growth is analyzed using different excitation frequencies and different formulations of a damage index while also assessing the influence of sensor failure on the algorithm. Moreover, an experimental campaign is performed during which composite plates are subjected to Quasi-Static Indentation (QSI) testing in order to create impact damage and monitored to obtain data.