Temporary flood protection measures are sometimes considered when flood defence structures fail to meet safety standards and full reinforcement is planned but not yet implemented. This thesis develops and applies a probabilistic decision framework to evaluate under which conditions such temporary measures are economically justified or required from a safety perspective during the interim period before reinforcement.

The framework combines a time-dependent cost–benefit analysis with a Loss of Life (LoL) safety threshold. Cost benefit ratio is calculated as a function of failure probability, potential damage, and the remaining time until reinforcement. The economic feasibility of temporary measures is evaluated by comparing their intervention cost with the accumulated avoided expected damage over the planning horizon. In addition, a Loss of Life criterion is included as an override condition to ensure that temporary measures are required whenever individual safety levels become unacceptable within ALARP principle, regardless of economic performance.

Application of the framework to the dike segment between the Maeslantkering and Rozenburg demonstrate how the proposed decision framework can be applied to a real-world case with a planned reinforcement horizon. The case study is used to illustrate the operational use of the framework rather than to derive generalised conclusions. The results show how the relative importance of economic and safety-based criteria can be explored within a single case through sensitivity analysis, without implying general dominance across different systems.

Extension of the analysis to a set of generalised polder archetypes in Chapter 5 explores how decision outcomes change across different combinations of system characteristics, including damage levels, baseline flood probabilities, and planning horizons. Uncertainty in key parameters, such as flood probability, damage magnitude, and probability reduction, can significantly shift decision boundaries. Decision maps and related visualisations prove useful for identifying regions where temporary measures are clearly justified, clearly unjustified, or sensitive to assumptions. These tools support transparent reasoning about trade-offs rather than prescribing a single optimal decision.

Overall, this thesis shows that temporary flood protection decisions can be evaluated in a structured and transparent way by explicitly linking cost, risk reduction, and time until reinforcement. The proposed framework supports consistent interim decision-making under uncertainty and is particularly relevant for managing non-compliant flood defences during transitional periods. ... Read more

This thesis investigates the potential of Augmented Reality (AR) headsets to enhance field technicians’ performance (in terms of efficiency and effectivity) in Operations and Maintenance (O&M) processes in Offshore Wind Farms (OWF), with Vattenfall’s operations serving as the case study. The key deliverable of this report has been the Business Case (Chapter 9), which provides a qualitative and quantitative analysis of the AR enhanced business process innovation. A critical gap for such empirical case studies was identified in the field of industrial AR applications, especially in offshore wind contexts, as noted in Chapter 1. The research is further motivated by the generation of a generalisable multi-framework approach for business process innovation. This approach evaluates the business case of novel technologies such as AR in capital-intensive industries, fitting in Vattenfall’s (generalisable) Stage-Gate innovation model (Chapter 2). Furthermore, societal impact is granted through accelerating the energy transition, by providing the first step in a business process innovation that should lower the O&M costs (which is about 30% of the Levelised Cost of Energy (LCOE, full lifecycle cost) (Bosch et al., 2019)) of renewable energy, improving its competitiveness against fossil fuels (Chapter 2). ... Read more

The thesis investigates how the EU’s new NIS2 cybersecurity directive, which establishes a unified framework covering 18 critical sectors, imposes demanding third-party risk management requirements on organizations. In particular, NIS2 mandates that organizations include supply chain security policies in their information security management systems and consider the vulnerabilities specific to each direct supplier and service provider in their risk assessments. These broad obligations create a major compliance challenge, as traditional TPRM practices are largely manual, fragmented, and non-standardized, causing duplicated efforts and inconsistencies in analysis.

To address this, the thesis asks: To what extent can third-party risk assessments be automated to enhance cybersecurity resilience under NIS2? To answer this, a mixed-methods research design was used. First, a systematic literature review and policy analysis established the problem context and identified gaps in existing TPRM processes. Second, in-depth interviews were conducted with cybersecurity consultants and tool vendors to provide practical insights into current TPRM workflows, data requirements, and automation opportunities. Third, leading commercial TPRM platforms were analyzed to identify their features and limitations. This combination of expert interviews, tool analysis, and literature review provided a comprehensive view of the technical and organizational factors affecting NIS2 compliance. Key findings show that many vendor assessment tasks (e.g., questionnaires, risk scoring, and monitoring) can indeed be automated, yielding faster, more consistent audits. For example, dynamic risk-scoring engines that aggregate questionnaire responses with external threat intelligence and past incident data can provide a continuous, quantitative view of each supplier’s security posture. Such automation substantially improves audit efficiency and accuracy compared to ad-hoc spreadsheet or survey-based processes. However, the study also found cultural and trust challenges: many organizations and vendors are still reluctant to share data or adopt common frameworks without strong governance.

Based on these results, the thesis recommends designing an integrated TPRM platform that centralizes vendor profiles, enforces a standardized assessment framework, and supports automated re-assessment and real-time monitoring. Vendors would upload a single security profile (covering certificates, policies, and controls) that can be reused across clients, avoiding duplicate questionnaires. The platform should support interoperability so that different tools and rating services can exchange information. For consultants, the implication is to shift from one-time static audits toward an ongoing advisory role within the platform ecosystem, helping interpret automated results and guide process improvements. For policymakers, the thesis suggests providing incentives and guidelines for interoperability and standardization (for example, common questionnaire templates for key vendor categories) to break down silos.

In conclusion, this work shows that automating TPRM under NIS2 is both feasible and highly beneficial. By embedding standardized data flows and machine-assisted analysis into vendor risk processes, organizations can more efficiently meet NIS2’s stringent requirements and improve overall cyber supply-chain resilience. The proposed recommendations automated platform architecture, policy levers for standardization, and a collaborative role for consultants provide a roadmap for practitioners and regulators to reduce manual efforts and strengthen third-party cybersecurity compliance. ... Read more

This study demonstrates the value of a structured, risk-based comparison between floods and pandemics - two disasters that, despite their fundamentally different origins, share a common risk profile as low-probability, high-impact, threat-driven events. Both hazards challenge societies due to their rarity and large-scale consequences, yet also allow for early intervention because of their forecastable nature. Despite extensive disaster literature, cross-hazard comparisons remain limited. The research question guiding this thesis is: What can be learned from a structured risk-based comparison of floods and pandemics?

To answer this, the study adopts a semi-qualitative, multi-method approach integrating literature reviews, expert consultations, and case analyses, structured around three sub-questions: comparing risk mechanisms, case timelines, and intervention effects.

The analysis reveals both fundamental differences and actionable overlaps. Flood and pandemic risk mechanisms differ at the source: floods stem from physical and meteorological causes, while pandemics arise from complex biological, ecological, and socio-behavioral factors. This greater causal complexity results in deeper uncertainty, making prevention, prediction, and early action more difficult for pandemics - even though swift intervention remains critical. Floods, in contrast, are more predictable, often allowing for threshold-based decisions and scenario planning. Prevention is the most effective risk reduction strategy for both, but less reliable for pandemics, meaning residual risk remains higher even with high effort.

Cross-hazard learning proves valuable in both directions: flood management offers lessons on structured planning and preparedness, while pandemic response underscores the need for adaptability, real-time data, and societal resilience. Ultimately, residual risk is inevitable - neither hazard can be fully prevented or contained. Defining acceptable risk levels, an established principle in flood governance but still largely absent in pandemic contexts, is important for transparent and effective disaster risk management. ... Read more

Large infrastructure development projects, such as airports, are significantly affected by the uncertainties in the geographic and socio-economic environment. Especially in the early stages of the project, little information about the final specifics of a project is available. As a result, accurately predicting the cost of such a project, as well as its financial feasibility, becomes a complex matter.
This thesis aims to develop a methodology framework, which can support the financial decision-making process in the early stages of airport development projects through an interactive tool. The methodology aims to use Structured Expert Judgment (SEJ) and Bayesian Network (BN) characteristics to provide insight into uncertainties surrounding airport development and its corresponding costs.
Two types of uncertainty are found to directly contribute to the financial risk associated with investment in infrastructure development, based on literature. One type pertains to the technical requirements for airfield pavements. The other type of uncertainty relates to price differences due to fluctuations and inflation.
The airfield pavements (the runway, taxiway, and apron) were determined to be the critical elements in cost estimation. Their dimensions and square meter prices were covered in a SEJ study to obtain probability distributions for these variables. The probability distributions were then combined with rank correlation coefficients calculated from a database with reference airports to implement in a Non-Parametric BN (NPBN).
The result of the research is a Graphical User Interface (GUI): DAiCE. This GUI enables the conditionalisation of the NPBN, predicting pavement dimensions and associated costs through simulation. The tool allows for input of project requirements and analysis of the project’s financial outlook.
DAiCE is proven to produce statistically significant results for airfield design issues. The simulated design values and cost estimates obtained from the tool are in line with those retrieved from reference projects. Though the tool is still bound to some limitations, such the included structures and exploitation models, it is concluded that the model of the airfield design landscape and its implementation in DAiCE can be used to support claims regarding the financial feasibility of an airport development project.
This thesis formulates several recommendations for future research, which are specified for SEJ, the construction and application of BNs, and further development of DAiCE. For SEJ, this includes investigation into different aggregation methods for SEJ, diversification of the expert pool, and expansion of the elicited topics. In addition, it is recommended to further develop the used software for constructing BNs, verification of model parameters, and exploring applicability of BNs in other research fields. Finally, expansion of the developed model, and additional functionalities for DAiCE are proposed. ... Read more

The public infrastructure manager Rijkswaterstaat (RWS) faces challenges in effectively preparing and tendering projects for renovation and replacement (R&R) of bridges in the Primary Highway Network (Hoofdwegennet, HWN) and Primary Waterway Network (Hoofdvaarwegennet, HVWN). The project delivery framework for R&R projects, the ”R&R Approach,” has shown limitations for identifying the full scope of and accurately estimate costs in R&R projects, resulting in financial setbacks, project delays, and heightened risks to the long-term functioning of critical infrastructure.
The main aim of this thesis has been to identify the primary complexities in bridge renewal that cause financial setbacks and what mechanism underlie them, so that the causes of scope change and inaccuracy in the cost estimation can be mitigated through enhancement of the project delivery framework.
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Asset management of flood defences systems includes strategic, tactic and operational decision levels. Since risk is a key parameter for asset management, risk management capabilities are important for the maturity and quality of flood defence asset management. The main objective is to develop and test methods for risk analysis in flood defence system management subject to deterioration and climate change. It focusses on three questions which elaboration can improve the risk-based management of flood defences, one at each of the three asset management decision levels.
The first is: How can the structural robustness of the flood defence contribute to flood risk reduction? An integrated risk analysis has been set up, valuing the risk reduction due to the structural robustness of a construction type, represented by its ductile behaviour during high loads. Therefore, the consecutive occurrence of initial dike failure mechanisms, failure path development, breach growth and consequences is modelled integral and time dependent.
The second is: How can planning of measures contribute to effective system risk reduction? Therefore, the interventions or measures are studied for a system of dikes in flood-prone areas, which are continuously required to mitigate changes such as ageing and climate change. A method is developed to compare different tactical plans to prioritize and plan measures in interdependent systems of dikes, to reduce risks most effectively and efficiently.
The third is: How can risk-based standards for flood defences reflect the benefits of structural robust designs? Therefore, it is studied how economic optimal probabilities of dike failure, can be updated to reflect the impact of structural robust dike designs. Furthermore, an analytical relation is developed for economic optimal design horizons. Finally, a dynamic and simple to use approach is developed to enable updating of the economic optimal reliability, based on a proposed design and planning. Therewith, it is practical possible to keep a dynamic focus on the optimal economic risk.
Risk analysis is an indispensable element in risk-informed decision making on each of the asset management decision levels used in asset management practices. The concept of a dynamic connected risk analyses is combined with the concept of decision levels, and the concept of the Deming circle as an organisational concept for continuous capability improvement. Coherent use, can bridge the practical disconnections between the decision levels.
Following the topics elaborating the questions, case studies are carried out showing the importance and impact. The main contribution of the work performed is that it provides a comprehensive perspective for the utilization of risk analysis as a tool supporting efficient flood defence system management.
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This thesis explores the integration of Maritime Autonomous Surface Ships (MASS) into Mixed Waterborne Transport Systems (MWTS), addressing critical challenges in ensuring navigational safety and operational efficiency. Recognising the complexities of interactions in MWTS, especially in scenarios without direct communication between vessels, the research develops a decision-making framework that integrates situational awareness, human-preference-aware navigation, and trust dynamics. These components collectively aim to support seamless interactions between autonomous and manned vessels, ensuring safe and efficient navigation in the MWTS. The proposed framework builds on a systematic exploration of key challenges in MASS operations. For situational awareness, an ontology-driven knowledge maps model is introduced, enabling MASS to integrate multi-source data and maritime regulations. This model is further combined with a Dynamic Window Approach (DWA) path planner, allowing for real-time compliance with COLREGs and proactive collision avoidance. The research also advances human-preference-aware navigation by extracting and modelling navigational behaviours of manned vessels using AIS data. An LSTM-autoencoder with clustering methods is utilised to identify navigational preferences, which are then incorporated into a trajectory prediction model based on Multi-Task Learning Sequence-to-Sequence LSTM with attention (MTL-Seq2Seq-LSTM-Att) architectures. This integration enhances MASS decision-making by aligning manoeuvring strategies with human operators’ expectations, reducing the likelihood of misinterpretation in mixed traffic scenarios. ... 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

The new Medical Device Regulation (MDR) addresses patient safety by mandating supporting clinical evidence for the sale and clinical use of medical devices. However, it places pressure on medical device manufacturers to supply regulators with robust clinical evidence to obtain regulatory approval of new devices they develop. Regulatory compliance is therefore more stringent, requiring more effort on part of both regulators and manufacturers. This thesis project explored the possibility of developing a new method to be used as a regulatory decision-making tool for the approval of newly developed knee implants. A Bayesian probabilistic method was developed and proposed. This method utilises existing arthroplasty registry and arthroplasty registry annual report data to determine if a newly released implant variant is “good”, where a “good” implant is one which meets standard regulatory thresholds in term of revision risk. This method was implemented on two datasets which were selected based on conditions of granularity, measures of implant performance, and accessibility for the thesis project. Some advantages and limitations of this method as a regulatory tool were proposed, the limitations being based on a clinical appraisal framework. Based on this it was concluded that the method indeed showed potential as such a regulatory tool, but needs further improvement due to considerations of reliability, precision, and the probabilistic nature of the results it yields. Both surgeons and patients can also potentially stand to benefit from this method. Aside from the regulatory context, also, the method has limitations in the fact that it does not account for other factors that may be responsible for complications related to knee arthroplasty surgery. Overall, however, this thesis presents a novel approach towards estimating the performance of knee implants from existing clinical data. ... Read more

Transboundary river basins are increasingly subjected to pressures from climate change, economic expansion, and population growth. These challenges are compounded by Deep Uncertainties and the complexities of managing water resources that cross administrative borders. The thesis aims to improve decision support for river basin management under Deep Uncertainty. Robustness Analysis is leveraged to improve the adaptive policy design of reservoir operating policies. Open Exploration generates future states of the world. Using Feature Scoring, PRIM, and Logistic Regression Modeling, Scenario Discovery pinpoints vulnerabilities that result in Adaptation Tipping Points described by streamflow patterns. Results identified decreasing precipitation and low seasonal amplitudes as the most significant uncertainty factors influencing system performance. In combination with the evaporation rate, they accurately predict policy failure. A precipitation threshold of 0.965 and a seasonal amplitude threshold of 1.05 effectively describe streamflow patterns that describe the Adaptation Tipping Point across the Best Hydropower, Best Environment, and Best Tradeoff policy. Specifically, a resultant streamflow pattern at these thresholds accurately signals imminent policy inefficiencies that necessitate policy adaptation. ... Read more

This master's thesis investigates the complex interdependencies between mobility and electricity sectors in the Dutch energy transition (2016-2050). Through comprehensive system dynamics modeling and scenario analysis, the research examines how Battery Electric Vehicle (BEV) adoption patterns interact with renewable energy integration. The study develops three major scenario analyses: exploring various BEV adoption rates combined with energy sector transitions, investigating different renewable energy generation capacities (120-180 TWh), and evaluating the feasibility of the Netherlands' 2030 zero-emission vehicle sales mandate. The findings demonstrate that while renewable energy capacity can accommodate increased electric vehicle demand, successful transition requires careful cross-sectoral coordination. The research contributes to understanding critical infrastructure challenges and provides valuable insights for policymakers and stakeholders involved in sustainable infrastructure development, particularly in the Dutch context. ... Read more

The current Dutch infrastructure construction market faces various challenges in renovations and sustainability, while the global context shows its volatility. The rising construction demand strengthens the contractors’ bargaining power, while they previously had to be (over-)accommodating towards the client to stay in business. Bad experiences from the past and the complexity of larger projects require a new approach to collaboration between (public) clients and contractors. Attention to the equitability of risk allocation can be a first step towards improvement. This topic is studied by analysing literature and five recent rail construction case study projects between Van Hattum & Blankevoort (contractor) and ProRail (public client), as well as interviewing practitioners. This research has aimed to create a risk allocation equitability assessment framework for construction projects to define, apply, evaluate, and improve risk allocation equitability in this context.

Combining several justice and responsibility theories provides a theoretical overview of the concept of equitability. This distinguishes distributional, procedural, interpersonal, and informational factors. Additionally, it considers contexts, collaboration, experiences, and expectations. Comparing the theoretical with practice shows that not all of these are relevant or (explicitly) applied by practitioners. The use of model agreements, such as the UAC-IC2005, is very influential in defining, applying, and experiencing equitability. As a result, the relevance of primarily distributional factors and contexts is affected. And although the UAC-IC2005 can serve as a benchmark and indicator for equitability, it cannot cover all factors practitioners experience as relevant for equitability. Therefore, this study has identified two points during the risk allocation process in construction projects when equitability is best assessed. A list of questions to assess relevant factors is provided. The scoring of these questions can be translated into a scoring of nine categories and plotted on a heatmap. This provides a comprehensive overview of how equitability is perceived and where attention is required.

Unfortunately, it is concluded that the perception of equitability is too subjective and multi-faceted to absolutely and undisputedly define, apply, and evaluate equitability. However, the findings also show that the question of equitability can, therefore, be neglected. The assessment framework drawn up in this study can provide a better understanding of what constitutes to equitability perception, a measure to identify equitability issues, and a guide to starting and directing discussions on this topic. Although the framework has, to some extent, been validated by contractor practitioners, it is recommended that this be repeated with the client’s side and that the framework be applied and evaluated to real-world projects. Furthermore, a quantitative approach to the topic and variation of the scope could deepen the understanding of equitability. ... Read more

Gaining insights into anticipated future expenses is essential for both the planning and construction phases of a project. A significant factor in future costs, particularly over extended periods, is the variability of material and labor prices. the change in costs over time, known as ’cost escalation,’ has been the focus of numerous research efforts. These studies primarily aim to forecast the Construction Cost Index (CCI), a composite index representing a standardized array of materials and labor typical in construction projects. This research presents a novel approach to forecast cost escalations, tailored to individual construction projects, addressing the shortcomings of predictions of a generic Construction Cost Index (CCI). Traditional CCI predictions, while providing some foresight, are limited by their generic nature and often overlook the specific material and labor variations within different projects. Additionally, current research generally fails to account for the uncertainty in the forecasts of the change in the material price and the estimate of the construction cost. In response to these challenges, this research pivots around the following research question: How can cost escalation for various types of construction projects be predicted, accounting for uncertainties in final construction costs and the forecast? Addressing this question involves selecting distinct indices for various project resources, such as steel, concrete, and labor, and combining these predicted indices in line with each resource’s cost. To achieve this, the study evaluates several time-series forecasting models, namely the vector error correction model (VECM), the vector autoregression (VAR) model, and the Holt-Winters model. For each model an automatic forecasting process is created, which automatically checks the suitability of the model, select the appropriate variables (in case of a multivariate model) and selects parameters. These models are evaluated for their accuracy across different time-frames and forecasting horizons. The Holt-Winters model, in particular, showed promise in providing reliable confidence intervals and point forecasts. The study’s testing phase revealed varying degrees of success, as volatile indices like copper and steel proved to be challenging, whereas forecasts for less volatile indices achieved higher accuracy. This outcome suggests room for improvement in refining these forecasting methods. The project-specific forecasts, including cost uncertainties, are developed by inputting cost estimates associated to the material price at current day value. The cost estimates input includes material and labor costs and their uncertainties. The uncertainties are represented through a three v point estimate (optimistic, pessimistic, and most likely values). This input is then transformed into a PERT distribution which is a transformation of the Beta distribution. The final stage involves combining the PDFs of each project activities cost (considering the cost of each specific resource) with the monthly forecast PDFs in a Monte Carlo simulation, providing a detailed cost distribution histogram of total cost and the individual resources cost. The tool’s functionality was demonstrated through a case study on a highway construction project. In this demonstration, specific project data, including material and labor cost estimates, were inputted into a hybrid web and Excel interface. This setup facilitates visualization and manipulation of project information. The tool processes these inputs via the AFP and Monte Carlo simulation, yielding comprehensive outputs such as histograms and statistical properties of the output. This is visible for total project costs and resource-specific escalations. This demonstration effectively showcased the tool’s capability to offer detailed insights into cost escalation, addressing the variability and uncertainty in construction projects. It underscored the tool’s alignment with the study’s objective of providing nuanced, project-specific cost escalation forecasts, moving beyond traditional CCI predictions. In conclusion, this study introduces a tool that caters to specific project resources, timelines, and uncertainties in cost escalations. While current limitations prevent its immediate practical application, this proof-of-concept lays the groundwork for future improvements. Focus of future research should be on refining accuracy and comparing the tool’s forecasts with escalation of historic projects to establish more robust insight into the actual escalation of projects. ... Read more

Because of the large uncertainties associated with rising sea levels, present coastal management is inclined towards utilizing soft, nature-inclusive, and adaptive measures over traditional hard protection structures. This shift towards more sustainable and multi-functional solutions is often called Building with Nature. In the coastal zone, these nature-based solutions typically use sand as a building material, have a larger scale than traditional sand nourishments, and serve multiple purposes. The definition of effectiveness of these nature-based solutions can vary for the different project aims. This means that models that predict future states of these interventions must be able to forecast various coastal state indicators (CSIs), such as dune volume, beach width, or habitat area. Using loose-placed sand in coastal protection is intrinsically associated with increased uncertainties of the coastal state compared to more traditional hard protection measures. Therefore, this dissertation examines uncertainty in predicting largescale sandy interventions in the coastal zone and its effect on different CSIs. First, observations of a recent large-scale intervention, the Hondsbosse Dunes, were analyzed to illustrate the evolution of various CSIs in the first years after placement. This 35 million m3 nourishment included a shoreface, beach, and dune and was built in front of a sea dike to serve as flood protection while creating space for nature and recreation. The nourishment created a significant coastline curvature, leading to erosion in the central, most protruding part of the nourishment, bordered by zones with accretion. The artificial cross-shore profile rapidly mimics the surf zone slope and beach width of adjacent beaches. At the same time, the dune volume increased, and the dune foot migrated seaward along the entire nourished site, regardless of whether the subaqueous profile gained or lost sediment. These contrasting trends of different CSIs highlight the need to predict changes in CSIs individually to assess a project’s effectiveness Next, the balance between different sources of uncertainty in predicting the evolution of a large-scale sandy intervention was investigated for the Sand Engine mega-nourishment. The sources of uncertainty in such predictions can be either intrinsic or epistemic. Intrinsic sources are inherent in the system, whereas epistemic sources are related to limitations in knowledge (related to the model). The relative importance of intrinsic and epistemic uncertainty was investigated using a probabilistic framework in which sediment transport is considered a function of random wave forcing (intrinsic) and model (epistemic) uncertainty, calculating transport using a one-line model. The applied wave climate variability was obtained from long-term wave observations, whereas model uncertainty was quantified using Generalized Likelihood Uncertainty Estimation (GLUE) relying on monthly observations. .. ... Read more

This dissertation focuses on studying the impact of Human and Organizational Factors (HOFs) on structural safety within the Architectural, Engineering, and Construction (AEC) industry. It is widely acknowledged that human errors are the primary cause of the majority of structural failures. In addition, HOFs are pivotal task contexts that shape human performance at work and contribute to the occurrence of human errors. Therefore, this research aims to study the critical HOFs in the structural design and construction process and analyze their influence on structural safety from a sociotechnical systems perspective. ... Read more

Critical infrastructures, such as the energy sector, are vital for the proper functioning of society. It is crucial to protect these infrastructures against cyberattacks on their operational technology, ensuring their operational performance, and safeguarding energy safety. There is a lack of existing research on improving cybersecurity in operational technology for increasing operational performance in liquefied natural gas infrastructure in the Netherlands, specifically when it comes to increasing redundancy in the system. In an attempt to fill this knowledge gap, this thesis implements agent-based modelling to answer the following main research question:

“To what extent does the implementation of redundancy enhance the operational performance of operational technology in the industrial processes of Gate terminal in the face of cyber threats, in order to maintain the availability of business services?”

The thesis shows that the key determinants for the effectiveness of a redundancy strategy are the criticality of the redundantly implemented element, and the degree of diversity applied. Additionally, a trade-off between the advantages of redundancy the inevitably increased attack surface can be observed: when implementing an ineffective redundancy strategy, overall system performance may degrade in comparison to the initial system conditions. The importance of an effective incident response cycle must not be overlooked. A redundancy strategy is only as effective as the capability of the system operator to prevent and deal with incidents.
Future research can build on this thesis, exploring more explicit redundancy strategies, types of attacks, and strategic defense systems, as well as the optimization of the redundancy trade-off. These research directions can help in further analyzing and understanding the impact of redundancy on system operational performance.
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Food safety inspections play an important role in the safety of consumers of food products. Even though food safety regulations have disciplinary power over food-related businesses, inspections are needed to ensure that regulations are followed for the well-being of consumers. Traditionally inspections did not have a goal of targeting higher risks. However, since 2004 EU regulations require all EU Member State inspectorates to follow a risk-based approach within their inspection plan. This is an effective way of inspecting and gives high results in catching violations. However, to be able to know what is happening in the overall industry representative results are needed, and performing inspections based on risks does not provide representativeness. Random inspections if performed in a representative way can provide this. Inspectorates are looking for ways to combine random and
risk-based inspections to get representativeness from risk-based approaches. To do this the inspection strategy might need to consist of a combination of random and risk-based approaches. The reason for this is to avoid bias that may result from performing risk-based inspections.
The main research question of this thesis therefore is: How can inspectorates adapt their inspection strategies to mitigate bias that results from risk-based inspections?
The exploration of how risk-based and representative inspections can be combined is aimed to
be supported with an investigation of methods that support this decision. This research aims to develop an assessment framework for evaluating such methods. In order to evaluate these methods, assessment criteria are needed that measure the methods appropriately. Since the reason to combine random and risk-based approaches is to reduce the assumed bias of risk-based inspections, there is a need to understand what bias is in risk-based inspections. Furthermore, to understand this, what risk means needs to be investigated. Therefore, the goal of the research is to find out how risk and bias affect the choice of inspection strategies, by investigating random and risk-based inspections and how these can be combined in order to leverage their advantages.
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The modern power grid is becoming more susceptible to cyber-attacks due to an increase in digitalization, leading to a larger attack surface for malicious actors to attack. Such attacks on critical infrastructure could lead to partial power outages, minor societal disruption, or in the worst-case scenario, a rolling black-out in which the entire country has no access to electricity. Electrical utility companies can decrease the likelihood of a successful cyber-attack on the Cyber Physical Power System (CPPS) – consisting of the physical power grid, and vulnerable Information Technology (IT) and Operational Technology (OT)- by implementing cyber security interventions. Investing in these cyber security mechanisms is not cheap, which is why it is expected to have a certain return on investment. However, it is hard to quantify the effects of prospective cyber security investments. The main research question of this study is: “To what extent can cyber security measures decrease the risk of cyber attacks on CPPS substations?” This research question is answered by means of an implicitly mixed research approach that uses computer-assisted attack tree modelling and Monte Carlo simulation. The model is based on the publicly available technical system information of known suppliers of relevant substation components and other documentation acquired by means of multiple literature studies and document analyses. The change in likelihood and subsequent risk has been studied by extensively modelling the possible attack paths of a digital substation. This has been combined with financial analysis in the form of a societal cost-benefit analysis. As a result, potential cyber security investments can be evaluated on their merits in the form of risk reduction and their required costs as expressed in dollars. The contribution of the performed research to science is the elaboration of existing models to more accurately represent reality, and simultaneously provide the cyber security decision-making process with a tool that provides guiding Key Performance Indicators (KPIs). This study has shown that suggested measures from the quantified model are able to increase the TTCavg needed by malicious actors to reach their intended target, and therefore cause a decrease in likelihood and subsequent risk of the studied scenarios. An important finding of this study emphasizes the need for extensive attack path modelling. This finding was the fact that the application of some well-intended countermeasure (such as remote-attestation), might have no significant effect on the likelihood and risk of a certain scenario at all, but only changes the dominant attack path. While the constructed quantified model, as proposed in this study, is able to provide quantified insights into the effects of proposed cyber security investments, it is merely a simplified tool that should be expanded upon to generate more accurate insights. Besides the aforementioned there have been additional findings from this study. Such as a list of weaknesses in the current state of digital substation cyber security. This list has been created by an extensive document analysis of over 40 sources. Also, an overview of 23 different possible cyber security interventions has been compiled by a systemic literature review of over 16 sources. According to the quantified model, a reduction (between 21.8% and 93%) in the total risk of certain attack scenarios against digital substation by malicious actors can be achieved. The costs for these possible risk reductions range between $28 thousand for a honeypot deception system and $413 thousand for a combination of all the simulated countermeasures. These countermeasures could, in comparison to a base case with no protection, potentially reduce the total risk by an amount between $3.7 billion and $15.9 billion. According to the general societal cost-benefit analyses, the best Retun-on- Investment (ROI)/cost-effectiveness of investment is the investment in a honeypot (scenario 5) which has an ROI of 247,390, and the least cost-effective is the investment in remote attestation (scenario 4), which has an ROI of -2,066. Altogether, this study has shown that there is added value in using a simplified quantified model to aid in decision-making for digital substation cyber security investments aimed at risk reduction. ... Read more

Currently, the Dutch National Police are looking to optimize resource allocation and decision-making through the measurement of the concept: “safety.” However, the measurement of safety does not have a universally defined method in the branch of policing and security. Qualitative data collection methods are being increasingly implemented in police departments around the world to improve safety and security. These data collection methods aim to implement evidence-based policing practices in order to form predictive assessments of future crime. Additionally, qualitative reporting methods such as officer interviews and victimization surveys can complement current quantitative data collection by improving police-community engagement and mitigating
"dark numbers" (unreported crimes).

This research aims to address what the implementation of victimization surveys and officer interviews, in coordination with current data collection methods, can add to an optimized police response and resource allocation to future crimes in theft for the Dutch National Police. The research approach of this thesis takes inspiration from commonalities found in a scoping literature review of policing methods around the world and a former joint interdisciplinary project (JIP) with the Dutch National Police. The perspectives of victims and police, which can be partially measured by surveys and interviews, are considered to significantly affect safety and security within society.

The research method was executed through the use of surveys (historical CBS data and theoretical scenario surveys) and officer interviews. These surveys and officer interviews were designed to determine how significantly certain victim factors, such as amount stolen, income, geography, and past experiences of theft affect a victim's perception on the severity of the theft, reporting threshold, and desired outcome in reporting theft. The method designed was flexible, as the ability to implement surveys to people that measures income and geography was dependent on both the Human Research Ethics Committee (HREC) and the data security concerns of the Dutch National Police.

Based on a victimization survey given to 1547 respondents, these victim factors do correlate with the perceived significance of theft. The magnitude and significance of these correlations are displayed in this report, and comparison with police employee interview results leads to significant insights that may determine why these correlations exist. In brief summary, the victim factors of income, geography, previous victimhood, and the amount stolen in theft generally have positive correlations with a respondent's victimization chance, reporting threshold, desired monetary compensation after theft, general satisfaction in successful reporting outcomes, and the perceived severity of theft.

A concluding recommendation from this research is to consider the further implementation of victimization surveys as a complementary data collection method. Specifically, the correlation of victim factors to the perceived significance of theft can assist in predictive policing through victim profiles that more accurately estimate dark numbers. In addition, the concept of adding qualitative measurement methods on victim factors to a universally-defined equation of safety can serve as a complement to current quantitative crime statistics. Through this implementation, current theft prevention and resource allocation strategies may be improved for the Dutch National Police, leading to a safer society. ... Read more