Twenty years after the introduction of Xbloc, DMC, which is a member of Royal BAM Group, introduced a uniformly placed and interlocking concrete armour unit named XblocPlus, which is suitable for straight to mildly curved breakwater sections, whereas Xbloc is currently applied on roundheads. Therefore, this study investigates modifications to the design or placement configuration of XblocPlus to enable its application on roundheads. Two concepts were tested on a H/V=4/3 roundhead slope for hydraulic stability in DMC’s wave flume. The first configuration consisted of regular XblocPlus combined with XP-Curve, a variant adjusted for narrowing radii, and XP-Base, a legged XblocPlus originally devised for bottom row applications. Tests with normative 0.02 wave steepness yielded a stability number (Hs/ΔDn) ranging from 2.72 to 3.09 due to uplift-related damage. The second configuration, featuring solely XP-Base and XP-Curve, resulted in a hydraulic stability number of 1.96 due to significantly less interlocking. No improvements were observed with respect to hydraulic stability compared to previous research. However, these designs notably reduced rotation-induced failure and enhanced applicability by enabling transition to the trunk and increasing maximum construction height. Additionally, this thesis investigated the validity of testing a three-dimensional structure in a 2D configuration, a method employed to maintain consistency with previous research. Wave heights in front of the structure were measured using parallel gauge sets, allowing the determination of the average disparity. This disparity was found to be within the acceptable margins for the principal test configuration. However, further research is needed to assess test conservativeness. In conclusion, this thesis provides recommendations aimed at improving the applicability and stability of XblocPlus on breakwater roundheads.

The Dutch government has set an ambitious target of reducing CO2 emissions by 55% by 2030, primarily through the development of offshore wind farms in the North Sea. While this transition supports sustainability, it also reduces available space for maritime traffic, increasing the risk of incidents. In response, this research aims to improve the detection of anomalous behaviour in the North Sea using machine learning, assisting Dutch Coast Guard operators in their monitoring tasks.
The primary focus of this study is to detect anomalous cargo vessel behaviour, specifically drifting, which has safety implications, as evidenced by the Julietta D. incident in January 2022. The research uses Automatic Identification System (AIS) data to monitor vessel trajectories and apply a machine learning-based anomaly detection model. This model uses features extracted from ship motion (e.g., speed, rate of turn), spatial properties (e.g. presence in anchorage area) and metocean condition (e.g., wave height) to detect anomalous behavior. The Local Outlier Factor (LOF) algorithm is employed to identify local outliers in a two-dimensional embedding of vessel trips, which is created using a density-preserving dimension reduction technique called densMAP.
The model successfully detects anomalous vessel behaviour within 30 minutes of occurrence, demonstrating its potential for real-time monitoring. In a case study of the cargo vessel Julietta D. incident, the model identified the drifting behaviour of this vessel, validating its effectiveness. The model also shows promise in detecting other types of anomalous behaviour, such as sudden changes in speed, and presence in defined areas.
While the model performs well in detecting a drifting incident and certain anomalies, it does not account for global outliers or identify other types of anomalous behaviour. Additionally, due to the limitations of AIS data, heading information was not incorporated. The research contributes to the safety monitoring of maritime traffic by providing a scalable, interpretable, and operationally feasible machine learning approach for the detection of anomalous cargo vessel behaviour.
Future work should focus on integrating time components, and supervised learning with labeled incident data to further refine the model. Ultimately, this research offers significant potential for enhancing operational safety and supporting the Coast Guard in the monitoring of maritime traffic in the North Sea.

The uncertainty paired with the effects of climate change impacts the design aspects of hydraulic structures. To comprehend the uncertainty of the evolving conditions, methods and models that differ from the traditional ones could provide a better understanding of the uncertainty and potential risks associated with a design.
Hydraulic structures are designed to withstand loading conditions related to extreme environmental scenarios. Such natural hazards are characterized by the shared dependence of several random variables. Multivariate probabilistic design clears the way for exploring dependencies between design variables. A multivariate approach models the joint occurrence of a combined condition, revealing new insights e.g., the correlation between wave height, wind speed and water level. Next to incorporating new models that estimate the uncertainty and changing conditions related to climate change, there could be a leap forward could in design methodology. Adaptive design strategies can provide a way to mitigate over-designing hydraulic structures. To cope with the uncertainty of sea level rise, this methodology aims at handling the uncertainty in long-term decisions by emphasizing adaptivity in a design.
This thesis aims to develop a multivariate probabilistic tool to provide insight into incorporating adaptive design strategies, and to gain extra information that contributes to assessing the feasibility of the Wide Green Dike concept given the uncertainty of sea level rise.
A multivariate model using a vine copula is created to approximate the joint behaviour of the extreme hydraulic boundary conditions of the Wide Green Dike project. The results showed that by accounting for the joint behaviour of the extremes and the uncertainty and variability of design conditions, significant reductions in the design could be realised compared to designing in a deterministic manner. The deterministic design increased the cross-sectional area of the dike by 150% compared to the multivariate probabilistic design with a sight year of 2150.
To explore the adaptive design strategies, several strategies are created using the multivariate probabilistic design approach. The strategies consist of an initial design with a certain sight year and one or multiple adaptions during its lifetime up to 2150. The strategies are assessed based on the business case, up-scaling of the clay refinery in the Ems-Dollard estuary, the impact on the Natura 2000 area and feasibility. It was found that incorporating an adaptive strategy could promote the business case and help mitigate the risk of over-designing.

Currently, inland waterway safety assessments rely heavily on historic accident data and expert opinions, often lacking comprehensive qualitative and quantitative information. Addressing this gap, this thesis introduces a method based on Automatic Identification System (AIS) data to identify anomalous vessel behaviour for enhancing safety assessments.

The proposed methodology establishes definitions of normal vessel behaviour and identifies deviations from these norms as anomalies. Analysing vessel trips recorded in AIS data logs, various features—including speed, acceleration, direction, manoeuvrability, and positional attributes—are extracted to define vessel behaviour.

The Uniform Manifold Approximation and Projection (UMAP) algorithm reduces the multidimensional features into a two-dimensional embedding to condense the vessel behaviour into a manageable form. This reduction technique preserves the inherent behaviour while representing similarities within a 2-dimensional space. Subsequently, the K-means clustering algorithm is applied to group vessels displaying similar behavioural patterns. The hyperparameters for clustering are determined using the elbow method and multiple scoring metrics.

Application of this methodology to the Moerdijkbrug at the Hollands Diep and Schellingwouderbrug at the IJ reveals clusters with similarities in vessel direction and paths. Several atypical patterns were observed and further investigated, analysing less than 1% of the data set in both cases, revealing two distinct patterns classified as probable accidents in the IJ case. These findings demonstrate the potential of the proposed method in identifying specific vessel behavioural anomalies with implications for safety assessment on inland waterways.