Rising sea levels and increasing storm intensity are driving the implementation of large-scale storm surge barriers globally to protect coastal communities and critical infrastructure. As these structures are increasingly located within major maritime access routes, their design introduces new navigational challenges that directly affect nautical safety. While extensive experience exists in the hydraulic and structural design of storm surge barriers, the navigational implications of alternative barrier configurations have received comparatively limited quantitative attention. Existing studies often focus on single configurations or rely on qualitative assessments conducted late in the design process, leaving a gap in systematic, configuration-specific methods that support early-stage design decisions.

The objective of this thesis is to develop and apply a quantitative assessment framework that enables systematic evaluation of the nautical safety performance of alternative storm surge barrier configurations during early design stages. The framework is grounded in the premise that, at this stage of design, nautical safety performance is most directly reflected in vessel maneuverability under constrained geometric and environmental conditions. Rather than attempting to predict accident probabilities or prescribe absolute safety classifications, the method focuses on configuration-dependent maneuvering demand and available control margins as necessary preconditions for safe navigation.

A structured assessment framework is developed that integrates spatial schematization of the navigational environment, critical environmental forcing scenarios, representative design vessels, fast-time ship maneuvering simulation, and quantitative nautical safety assessment metrics. These metrics describe maneuvering performance in terms of spatial, temporal, and control margins, enabling reproducible and configuration-specific comparison. Fast-time simulation is employed to ensure computational efficiency and repeatability, making the framework suitable for iterative application during early design phases.

The practical applicability of the framework is demonstrated through a case study of the proposed Bolivar Roads storm surge barrier in Texas. Multiple alternative barrier configurations are evaluated using the fast-time simulation model SHIPMA. Simulations are conducted for a set of representative design vessels under selected flood and ebb tidal conditions, using consistent spatial schematization and environmental assumptions across configurations. Simulation outputs are post-processed to derive the values of the quantitative safety metrics for each configuration.

The results show that nautical safety performance is highly sensitive to barrier geometry. Configurations featuring wider gate openings and more favorable alignment and siting consistently exhibit larger spatial and temporal maneuvering margins, reduced control effort, and more stable vessel behavior. Conversely, configurations with constrained openings or unfavorable alignment impose increased maneuvering demand and reduced controllability. These findings demonstrate that geometric design choices can substantially influence navigational safety performance and that such effects can be captured quantitatively using the developed framework. While the absolute values of the assessment metrics are subject to modeling assumptions and simplifications, the relative differences between configurations provide meaningful insight for comparative evaluation. As such, the results are not intended to replace expert judgment but to serve as structured input for pilot and stakeholder discussions, supporting transparent and informed interpretation of navigational safety implications.

This research contributes a systematic, maneuvering-based framework that bridges the gap between hydraulic design and nautical safety assessment in storm surge barrier projects. By enabling early-stage, quantitative comparison of alternative configurations, the framework provides capabilities that were previously unavailable in design practice. The case study illustrates the framework’s practical value and transferability to other navigation-constrained barrier locations. Future research should prioritize integration of configuration-specific hydrodynamic modeling, enhanced representation of human factors, and further refinement of assessment metrics to improve physical realism and decision relevance. ... Read more

In the coming decades, the shipping sector is facing various challenges, requiring adaptations for achieving sustainable shipping, against climate change consequences, for facilitating alternative activities at sea, and for transitioning towards more autonomous shipping. Several incidents related to these challenges force us to take a good look at how the system can keep performing its function conditional to these changes. Scientific studies hereby regard the collective of (interacting) shipping activities as a system. Outcomes of data analyses and models are intended to support decision makers in designing effective improvement measures. However, the usefulness of the outcomes to the decision makers can be better, amongst others due to poor communication between science and decision makers, due to analysis objectives not being achieved, and due to unrealistic data requirements.

At the foundation of the analysis is often a disciplinary approach, or \textit{way of thinking}, which determines which solution space is considered, and which input sources are accepted. Looking from multiple \emph{perspectives} can broaden this, and thereby improve the formulation of analysis objectives and the identification of relevant input data. Besides determining which perspectives are relevant for a specific problem, the remaining challenge is related to how these alternative perspectives can be merged into an integrated whole. The aim of this thesis is to design a framework for an early integration of multiple perspectives in the analysis of shipping systems to improve their usefulness in the decision-making process. The first ambition for the framework is to provide a formulation of analysis objectives and data requirements in view of multiple perspectives, and the second ambition is to develop a data-structure concept to merge the perspectives.

For the first ambition, a literature study into systems with similar characteristics as a shipping system revealed that the analyses of these systems are mostly performed from one or several of the perspectives regarding its objectives, that we refer to as: (1) \textbf{scales}, addressing the ``where'' and ``when'' of system performance, uncovering spatial patterns and temporal variations, (2) \textbf{conditions}, considering the connection between system performance and its underlying physical processes and environment, (3) \textbf{behaviour}, considering the influence of individual or collective behaviour on the system performance and (4) \textbf{dependencies}, identifying causal relationships and sensitivities within the system. For each of the distinguished perspectives, based on the data sources and analysis types of the relevant studies, specifications could be formulated about the highest detail level on one hand, and the information required to aggregate to higher levels, up to the system level, on the other.

The second ambition, regarding a concept for merging these multi-perspective requirements, was obtained by introducing a new data structure referred to as an \emph{event table}. In this data structure, inspired by the existing concepts of moving features and event logs, each row represents a distinct event, and each column indicates a characteristic of the event. A single event is defined by the highest-detail-level specifications for each perspective. Besides some columns that form the unique event definition, the \emph{attributes} provide additional information about each event. Filtering and aggregation operations on the event table allow zooming in and zooming out, offering flexibility to investigate global patterns in detail, or to assess the impact of detail level processes, thereby fulfilling the second ambition for the framework.

The framework outlines the relationship between the availability of input materials and the ambition of the analysis goals. Hence, developments in the field of data science, analysis techniques, and computational facilities increase the scope, detail level, and modeling complexity captured in the analysis goals. By parallelising and scaling-up computations, the scope and detail level of analyses can be increased. By joining multiple spatially and temporally varying data sources, environmental influences can be determined. By applying dimension-reduction and outlier detection techniques, many characteristics of vessel behaviour can be assessed to determine anomalous behaviour. By labelling known behaviour, cause and effect can be coupled to improve the predictive capabilities. Applying these developments to the monitoring activities regarding nautical safety demonstrated how these developments can extend the ambition level of the analysis.

The framework was applied to two shipping-related cases. The first case considered nautical safety risks at the North Sea imposed by the potential event that vessels get adrift while being surrounded by offshore infrastructure, like wind parks. Based on the formulated multi-perspective objectives, the event table was constructed, whereby each event was defined by combination of a vessel of particular type and size (indicated by a category), to be present at a particular location at sea (indicated by a cell, part of a grid), under particular environmental conditions (a combination of wind direction, wind speed, wave height-period combination, wave direction, and current profile). For each event, the probability of occurrence could be determined, and conditional to this, using a drift path prediction tool, the probability that the vessel would drift into a wind park after $n$ hours in case of technical problems. Filtering and aggregation operations on the table revealed how a single analysis can support location specific design of barriers between wind parks and shipping lanes, as well as evaluation of strategies for emergency response vessels.

The second case considered shipping emissions on Dutch inland waterways. Based on the framework, analysis objectives were formulated for three perspectives; scales, conditions and behaviour. This resulted in an event table whereby each event corresponded with a single vessel, sailing a single waterway section on the Dutch fairway network. For each event, based on the sailed trajectory, the vessel properties, and the environmental characteristics, the energy use as well as the associated emissions could be estimated. The entire collection of events in the table represented all vessels travelling on the Dutch inland waterway network over the course of four months. Filtering and aggregation operations on the table revealed how emissions are impacted by river currents, and that a large share of the emissions is caused by waiting, idling, and manoeuvring vessels.

Both cases demonstrated how application of the framework can lead to an improved understanding of how the shipping system performs and responds to varying conditions and external changes. More importantly, they showed that the event table concept was capable of supporting formulation of promising improvement measures. This offers policy makers better support when making decisions. Owing to the versatility of the event-table concept, it is possible to anticipate on unseen or unforeseen perspectives in the future. ... Read more

Maintaining the channels and infrastructure for large commercial ports is costly and complex. Port authorities traditionally rely on bathymetry surveys to guide their dredging operations, which limits their ability to be proactive. This research aims to develop machine learning (ML) methods to predict sedimentation rates (SR) by analysing patterns in hydrological and meteorological (hydro-meteo) conditions and dredging data. By investigating the possibility of SR prediction models, this research can contribute to efficient maintenance operations.

The study focuses on the Botlek, a harbour in the Port of Rotterdam situated in the Rhine-Meuse estuary. Three data types are integrated to capture the dynamic interplay of saline and riverine factors within the estuary. The data consists of Multibeam bathymetry surveys, hydro-meteo variables (such as salinity, river discharge, and tidal variation), and dredging logs. The surveys provide the net sediment accumulation, which is assumed to result from a specific period of hydro-meteo conditions and dredging. Due to the limited availability of surveys, the number of features had to be managed. To avoid having more features than samples, the hydro-meteo variables are aggregated into daily, weekly, and
monthly means.

The ML algorithms evaluated in this research are Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR). The feature importance scores from the RFR and the accuracy on small datasets of the SVR were decisive in this selection. All algorithms were tested and refined over multiple development phases to determine their predictive accuracy and robustness across different data configurations. It was found that the ML algorithms can reasonably predict SR. In addition to dredging data, incorporating hydro-meteo variables enhanced the predictive accuracy and consistency. Specifically, a feature set of dredging volumes, salinity, discharge, and tidal variation improved model performance. Training the model on dredging data only resulted in an inferior performance. The monthly aggregation of these hydro-meteo data was the most beneficial configuration. Among the tested algorithms, SVR consistently outperformed both LR and RFR, with its best configuration achieving a mean R2 score of 0.69 over four different dataset splits.

The ML models do not have to be able to predict sediment volumes with a small confidence interval to be practical for the port authorities. Instead, the models should be able to predict trends in sediment accumulation and provide actionable insights to enable proactive dredging. The research shows promising results, as most predictions fall within the acceptable range. However, predictive accuracy strongly varied across the different dredging areas in Botlek. Further development is required to provide real-time, reliable SR predictions before the models can be integrated into the maintenance operations of ports. ... Read more

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.
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This thesis introduces a new algorithm for optimising shipping routes within a dredging project. Highly dynamic and time-dependent hydrodynamic features influence shipping routes. Due to the complex interactions between the horizontal tide, vertical tide, stratifying forces, wind-driven forces, and limited water-depth, shipping routes were previously only optimised for large scale routes (order of 1000 km). This study presents an algorithm that can optimise shipping routes that are influenced by these small scales (order of 10 km) hydrodynamic features. This algorithm uses graph theory to solve for the time-dependent fastest path between start and destination. Graph theory searches for the optimal path through a set of nodes that are connected with edges. This study uses the time-dependent shortest path algorithm which accounts for the FIFO-criteria (Waiting criteria) and can solve the non-convex nature of the problem

The input of this algorithm is a hydrodynamic model. These models are Computational Fluid Dynamic (CFD) models that calculate currents and water levels in a specific domain. The domain is discretised into cells and nodes to calculate these hydrodynamic features. This study uses the nodes of this hydrodynamic model as the vertices of the graph. However, for some cases, the hydrodynamic model has too many nodes for the shortest path algorithm. This study presents a method for reducing the number of nodes without reducing the spatial resolution. The nodes are reduced based on a combination of the vorticity and the magnitude of the flow.

This algorithm is implemented in a python software package named Hydrodynamic Algorithm for Logistic enhancement Module (HALEM). HALEM can determine the optimal shipping route for a given hydrodynamic model. Defining different cost functions results in different optimisation purposes. This thesis presents cost functions for the fastest route, shortest route, cheapest route and least polluting route. This software is then implemented in the OpenCLSim software so that this combination of software can optimise routes of entire projects. A case study simulates a beach-nourishment at Schouwen Westkop Noord to demonstrate the practical use of HALEM and OpenCLSim. For this project, 425,500 m3 sand should be dredged offshore and pumped onto the beach. Due to the narrow gullies and tidal changes in hydrodynamic features, the routes were hard to predict. The simulation with HALEM and OpenCLSim shows an increase in the production with 21 % compared to the simulation with just OpenCLSim.
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Vector-based graphics are not directly compatible with the raster-based structure of dedicated graphics hardware. They suffer from a locality problem where, in order to check if a specific display pixel lies inside or outside the shape, the entire shape needs to be taken into account. This negates the advantage that the graphics pipeline’s parallel rendering structure offers. Current solutions rasterize vector graphics into a discrete raster-based texture in an extra render pass, every time the required texture resolution changes.
This work presents an approach to store vector-based shapes into discrete raster-based textures, optimized for parallel rendering. This is done by encoding the shape as piece-wise cubic Bézier curves, structured in a quad-tree. Additional data is added to remove aliasing effects in case of minification. These textures are then rendered by a custom fragment shader.
This work discusses the implementation of both the serialization and deserialization steps, as well as rendering regular vector shapes with a comparable quality. ... Read more