Flooding is one of the most devastating natural hazards, with increasing frequency and severity projected under climate change. Adaptation of the built environment has traditionally emphasized “hard” measures, such as levees and barriers, while “soft” measures, aimed at strengthening social and behavioral resilience through regulatory, educational, and community-based strategies, are less well known and often neglected. Although both are needed, the capacity of soft measures to increase resilience in isolation or combined with hard measures remains unclear, limiting their wider application and formal integration into the processes of resilience management.

This project first clarifies the concept of soft adaptation measures, providing a consolidated definition and categorization that distinguishes their types, benefits, and limitations. Furthermore, it explores how soft measures perform in the urban space, and how they interact with one another and hard interventions, strengthening the argument that integrated (hybrid) strategies are more effective in enhancing urban resilience to extreme weather events. The findings of this research contribute to both academic understanding and practical policy development. By establishing a clearer conceptual foundation and practical framework for soft adaptation, the study facilitates more coherent adaptation planning in the urban space.

Wildfire simulations have become increasingly important as their frequency and severity increases, posing a threat to communities and resulting in billions in damages. However, current wildfire models face a trade-off between accuracy and computational complexity. Current wildfire models can be physics-based, but computationally expensive, or based on empirical data, which allows for better computational speed, but decreases the physical basis. A model combining the computational speed of empirical models with the physics understanding of physics-based models to increase the accuracy of wildfire simulations is desired. This master thesis explores the design of such a model by answering the question: ’How can a near real-time wildfire simulation be designed using physics-informed machine learning?’....

Storm surge barriers, a critical component of the Dutch Delta Works, faces growing operational challenges due to climate-induced sea-level rise and increased storm intensity. These developments are expected to lead to significantly more frequent closures, creating not only ecological and economic strain, but also maintenance and societal tensions. While the technical and environmental consequences of these closures are well documented, the question of societal tolerance remains under-explored. This research addresses that gap by investigating how societal tolerance can be assessed and integrated into the governance of storm surge barriers, using the Eastern Scheldt as a case study. The Social Licence to Operate (SLO) framework, originally developed in the extractive industries, is adapted to conceptualise public acceptance as a dynamic, multi-level condition. Key drivers of societal tolerance, including trust, risk perception, ecological dependency, and communication, were identified through academic literature and 15 semi-structured interviews with stakeholders from aquaculture, recreation, governmental, and environmental organisations. Findings reveal that stakeholder tolerance is highly sector-specific and shaped by governance strategies. Aquaculture actors express low tolerance, bordering on withdrawal, while environmental NGOs offer conditional acceptance based on ecological commitments. A conceptual framework is developed around four boundary conditions:knowledge, legitimacy, credibility, and trust, each linked to stakeholder positioning and actionable policy levers. The study offers both a theoretical extension of the SLO-framework and practical guidance for infrastructure governance. Recommendations include implementing a tolerance monitoring dashboard, embedding ecological thresholds into operational planning, and fostering long-term participatory engagement. Ultimately, the research promotes a shift from reactive to anticipatory governance, positioning societal tolerance as a critical parameter in sustainable asset management.

The design of 2D rectangular steel trusses demands a critical balance between structural performance and constructability, a core challenge in civil engineering. Using distinct cross-sectional profiles minimizes material use but elevates structural complexity, whereas standardized profiles facilitate construction simplicity at the cost of efficiency. This thesis develops a multi-objective optimization framework to navigate these trade-offs, targeting four essential objectives: mass minimization to reduce material requirements, connection degree to simplify joint configurations, symmetry to enhance aesthetics and standardization, and beam continuity to streamline assembly processes. By controlling the number of unique HEA profiles, the study delivers tailored solutions for preliminary structural design, aligning with engineering priorities and stakeholder preferences to optimize truss performance and practicality.
A computational framework employs the Tree-structured Parzen Estimator (TPE), a sample-efficient Bayesian optimization method, to efficiently explore the complex, discrete design space of truss configurations. TPE performance is rigorously validated against exhaustive search (EXS) to ensure accuracy in identifying optimal designs. Stakeholder-defined weights, implemented through weighted scalarization, enable customized trade-off analyses, though without direct stakeholder engagement. This approach supports the exploration of diverse configurations, effectively balancing performance and standardization while addressing the computational demands of large search spaces, thus providing a robust tool for 2D truss optimization.
The findings indicate that intermediate profile grouping often produces designs that balance structural performance and constructability. The multi-parallel plot, a dynamic visualization tool, potentially empowers stakeholders, including engineers and project managers to transparently explore trade-offs, pending practical validation. Despite limitations, such as untuned TPE hyperparameters and a focus on 2D trusses, this promising framework enhances transparency and adaptability in preliminary structural design. By integrating efficient optimization with intuitive visualization, the study establishes a foundation for future advancements in steel truss optimization, offering a versatile methodology with potential to inform broader structural engineering applications.

The ever-increasing size of offshore wind turbines, combined with the development of wind farms at more remote sites with deeper waters and more extreme weather conditions, presents significant logistical challenges. Furthermore, the offshore wind sector faces reduced government subsidies, narrow profit margins, and a lack of industry guidelines, all while striving to lower the levelised cost of energy in order to remain competitive in the market. Developers must navigate complex decisions regarding foundation selection and installation strategies. However, existing research lacks systematic approaches to optimise these decisions, with particular gaps in studies addressing multi-vessel operations, vessel compatibility with component size, alternative transport vessels, and the specific challenges of substructure installation. This lack of structured frameworks hinders evidence-based decision-making in foundation selection and installation planning. This research develops a framework to address the question: How can a multi-vessel optimisation framework integrating alternative transport vessels and accounting for weather uncertainty improve foundation selection and installation scheduling to minimise costs?  The first phase of this study develops a deterministic decision tree model that evaluates foundation selection based on environmental parameters and identifies cost-optimal Transport and Installation (T&I) strategies. Analysis of 23 previous installation projects highlights water depth as the primary decision factor, with a 50-metre threshold distinguishing between monopile foundations for shallower waters and suction bucket jackets for deeper waters. A 127-nautical-mile threshold marks the transition from shuttling to feeder strategies. Cost sensitivity analysis reveals that water depth significantly impacts cost, while seabed conditions show minimal influence. The decision tree model’s foundation type prediction accuracy is 43.5%, reflecting the complexity of real-world decision-making. This phase provides a visualisation of pathways for early-stage project decision-making, emphasising the importance of more detailed installation schedule optimisation using Mixed Integer Linear Programming (MILP) and stochastic approaches.  The second phase employs MILP to optimise installation scheduling for a case study and assess the installation performance of monopiles versus pin-pile jackets using the assembly-line installation strategy. The deterministic model incorporates constraints (e.g., task precedence, operational, deck capacity) and weather limitations using historical site weather data, while a stochastic extension models weather uncertainty using Weibull distributions. The performance of monopile-transition piece (MP-TP) strategies is compared with pin-pile jacket (PP-JK) strategies, focusing on weather sensitivity, costs, and computational performance. In addition, logistic setups are evaluated by varying the number and type of vessels used, from three to five. The results show that MP-TP strategies outperform PP-JK strategies, with MP-TP installations preferring shuttling over feeding when a 2.5-metre wave height limit is applied. For PP-JK installations, Heavy Transport Vessels (HTVs) are preferred over barges due to their higher deck capacity, despite the lower installation rate of the associated installation vessel. The use of a single installation vessel capable of installing all component sizes is found to be more cost-effective than using smaller and cheaper additional vessels. Weather uncertainty significantly influences installation scheduling, as shown by both deterministic and stochastic models.  The developed decision support tool provides a basis for further research in offshore wind logistics and other industries. Although the findings are applicable within the scope of this study, future research should explore additional factors such as stochastic risk assessments for pile refusal and assess the impact of larger wind farm sizes and dynamic port-to-farm distances.

Water Distribution Networks (WDNs) play a pivotal role in maintaining urban resilience, especially in the aftermath of seismic disruptions. Despite their importance, existing resilience assessments for these systems face limitations, including the lack of a consensus on the definition of resilience and insufficient emphasis on the recovery process. This work evaluates the seismic resilience of WDNs by developing an optimization model to prioritize pipeline repair sequences. The optimized sequence is then used to generate the characteristic curves of the network, offering an assessment on its overall performance. The optimization is carried out using a Genetic Algorithm, while various scenarios of seismic intensities and available resources are considered to generate the characteristic curves. To assess scalability and broad applicability, the model is tested on WDNs of varying sizes. The results offer key insights into the resilience of WDNs, reflecting their intrinsic behavior under seismic stress. This integrated approach—linking resource distribution, repair scheduling, and resilience metrics—can guide asset managers, engineers, and policymakers in both risk management and recovery planning for WDNs in seismic-prone regions.

The Netherlands faces a significant challenge in maintaining its estimated 85,000 bridges, most of which were constructed between the 1950s and 1970s. Addressing this "Replacement and Repair Challenge" requires prioritizing bridges at high risk of deterioration to optimize maintenance efforts. Internationally, predictive maintenance studies have successfully utilized Bridge Deterioration Models (BDMs) that leverage inspection and supplementary data to train machine learning models for predicting bridge deterioration. This study investigates whether Dutch bridge inspection data, collected under the NEN 2767 standard, could similarly support BDMs for predictive maintenance. The aim is to evaluate the suitability of NEN 2767 data for this purpose and identify necessary modifications to enhance its predictive capabilities. Data from five provinces and seven municipalities were analyzed using the "4 Vs" framework: Volume, Variety, Velocity, and Veracity. Results indicate that, due to data inconsistencies, limited feature diversity, and insufficient volume, the Netherlands is not yet prepared to apply machine learning to NEN 2767 data. To explore these challenges further, semi-structured interviews were conducted with five government agencies and four inspectors. Findings suggest that while there is confidence in the NEN 2767 standard, significant variation exists in data collection and storage methods. Furthermore, maintenance decisions rely on additional information that is not consistently recorded in government databases alongside NEN 2767 data. A literature review on BDMs identified 25 critical features that could improve the predictive accuracy of NEN 2767 data for Dutch bridge deterioration. Based on these insights, four key recommendations are proposed. Firstly, preliminary recommendations were presented to stakeholders, after which some adaptations were maded to improve their quality. These for recommendations are as follows: (1) Extend the CUR 117 standard to enhance data collection, storage, and sharing protocols; (2) Develop a standardized Inspection Procedure, which would involve certification through the CUR 117 commitee; (3) Incorporate the 25 additional features identified as relevant for predictive maintenance; and (4) Utilize the Schouw, an annual inspection process, as a means to capture more maintenance data on bridges. These steps would collectively strengthen the predictive capabilities of NEN 2767 for proactive bridge maintenance in the Netherlands.

In the face of climate change, reinforced concrete (RC) bridges encounter significant risks, indicating the need for effective adaptation measures. This paper explores the co-benefits of climate change adaptation for RC bridges and how sustainability and resilience can be achieved through adaptation measures. It also investigates the integration of climate change adaptation into Eurocode standards. The chosen approach includes mapping and analysing (i) climate change impacts on RC bridges and (ii) the co-benefits of different adaptation measures. Moreover, it includes an examination of the current generation of Eurocodes and an exploration of barriers and proposed actions towards climate change adaptation. The research highlights the complex relationships and compound effects characterising climate change impacts on RC bridges and the co-benefits of several adaptation measures. Moreover, it emphasises gaps in existing Eurocodes that need to be addressed to achieve climate change adaptation. Future actions, such as the economic viability of adaptation measures, their long-term performance, and the trade-offs of the bridge’s life extension, especially for measures non-sustainable at first glance, should be further investigated.

As a result of growth in the offshore energy infrastructure, the connection between the offshore environment and the mainland is increasing in importance. This leads to an increased use of the seabed for cables and pipelines as part of our energy infrastructure. Cables or pipelines are placed in trenches and covered by sediment, to be protected from any damage from activity near the sea bed. During execution, the trenches will refill with sediment prior to the placement of the cable or pipeline, which is known as siltation. The siltation rates are increasing when entering the breaker zone near the shoreline. The predictability of the siltation rates are a crucial step in efficient and safe realisation of new connections between the offshore energy infrastructure and the mainland.

Inclusion of nearshore processes is missing in existing quick-assessment siltation tools. Complex process-based models, like Delft3D or XBeach, are capable of predicting siltation volumes in the nearshore environment accurately. However, these models demand large computation capacities. This makes them unsuitable for probabilistic modelling, requiring large numbers of calculations. Probabilistic modelling however is a crucial step in identifying and quantifying uncertainties and related risks in the execution. This research presents a quick-assessment tool that includes nearshore processes. Ensuring low complexity makes quick-assessment tools suitable for probabilistic modelling of siltation predictions, reducing and quantifying uncertainties within nearshore trench siltation.

This research presents an approach to include wave transformation and wave-driven currents into an existing siltation prediction tool (SedPit). The resulting SedPit Nearshore tool allows fast predictions of siltation volumes in the nearshore zone. The performance of the SedPit Nearshore tool is assessed by comparing it to data from a field case, and comparing the accuracy to the accuracy of the existing SedPit tool. The SedPit Nearshore tool gives accurate predictions on the total siltation volume, and gives good insights in the spatial distribution of siltation volumes. The potential of the SedPit Nearshore becomes most evident when comparing it to the existing SedPit tool. A great improvement compared to the existing SedPit tool is seen. For the test case, the SedPit Nearshore tool reduces the absolute error in redicting the total siltation volumes by 82% compared to the existing SedPit tool. The SedPit
Nearshore tool predicts the total siltation volume with an error margin of 7%, while the existing SedPit has an error margin of 41%. The largest improvements compared to the existing Sedpit are seen in the most onshore regions, as this is the zone where most wave-driven currents are generated. The computational speed of the tool has proven its applicability for analyses on model sensitivity and uncertainty quantification. Computation times are reduced by factor 9,000 when comparing it to XBeach, a complex process-based model. Bottom roughness ks and wave roller steepness β were identified as most influential free variables in driving nearshore siltation volumes. Calibrating the model to obtain likely values for a range of free variables has helped to reduce the 95% confidence interval of peak siltation rates by 36%.

The inclusion of wave-driven currents into existing siltation prediction tools has shown a great improvement in the accuracy of siltation predictions in the nearshore zone. Although the SedPit Nearshore tool is calibrated on one specific field case, the method and the workflow of the tool show potential to help as general prediction tool of nearshore trench siltation. The power of the SedPit Nearshore tool lays in its simplicity, making it a fast, efficient, and accurate tool, suited for probabilistic modelling.

The study aims to develop an optimization model that determines the optimal configuration of dynamic Electric Road Systems (ERS) and static charging infrastructure for heavy-duty electric trucks. By considering varying levels of ERS adoption, the model seeks to minimize total infrastructure and operational costs while maximizing demand coverage along key transport routes. The research uses a bi-level optimization model: the upper level addresses government decisions on infrastructure placement to minimize infrastructure costs, while the lower level focuses on user routing to minimize transportation expenses. The model was applied to the Netherlands as a case study, optimizing the placement of ERS and static chargers based on traffic patterns and user behavior. Key findings indicate that ERS and static chargers are complementary, with ERS proving more cost-effective on high-traffic routes, reducing battery size and eliminating charging downtime. In low-traffic areas, static chargers provide essential infrastructure support. The model demonstrated that an integrated charging network could lead to cost savings of up to 25%. The study concludes that a combined ERS-static charging infrastructure is the a cost-efficient approach for electrifying freight transport, offering both economic and environmental benefits .

As societies worldwide grapple with the urgent need to mitigate carbon emissions, one domain where substantial strides can be made is heavy-duty road freight transport. Electrification has emerged as a practical and technologically mature solution to address this challenge. However, the widespread adoption of electrification in this sector is met with formidable obstacles. These obstacles encompass the inadequacy of charging infrastructure, restrictions in driving range on a single charge, the demand for robust and high-capacity onboard batteries, and the looming specter of potential battery shortages.

Within the European context, discussions revolve around sustainable solutions that can pave the way for a cleaner and greener future. Among the contenders in this realm, the electric road system (ERS) has risen to prominence. ERS introduces a groundbreaking concept where trucks can recharge their batteries while in motion on highways, promising an array of ecological and economic benefits. However, the journey toward the implementation of ERS infrastructure is not without its intricacies. It necessitates the installation of specialized charging infrastructure, which can take the form of overhead catenaries accessed by a pantograph or embedded road equipment. Moreover, there is a substantial financial commitment required to equip entire truck fleets with the necessary batteries, adding to the complexity of the endeavor.

The central challenge in this landscape revolves around the meticulous design of an optimal ERS network that adeptly balances infrastructure costs with battery expenses. This research aims to address this multifaceted challenge by posing a fundamental question: How to determine the optimal ERS network, given the trade-off between infrastructure and battery costs?

To tackle this question head-on, this paper introduces a sophisticated multi-objective optimization model. This model is a computational framework that concurrently minimizes the costs associated with infrastructure investment, encompassing the installation and maintenance of ERS components, and the total transport expenses. These total transport costs encompass a range of factors, including the procurement of batteries, energy consumption, and toll charges. This comprehensive approach takes into account the diverse perspectives and interests of both investors and logistics companies, providing a holistic view of the intricate challenges associated with ERS adoption.

One pivotal advantage of ERS becomes evident in its capacity to extend the lifespan of batteries by reducing wear and tear during typical driving conditions. The model thoughtfully incorporates this aspect, factoring in battery purchase costs that hinge on projected lifespans. These projected lifespans, in turn, are influenced by the chosen route's electrification rate (ERS implementation).

To validate the model's effectiveness and practicality, it is subjected to a rigorous real-world case study. This case study delves into the intricacies of road freight transport in Germany, the Netherlands, Belgium, and Luxembourg. Additionally, this research introduces an enhanced Genetic algorithm, complemented by an Elitism strategy. These enhancements are designed to optimize solutions effectively within the confines of this practical context.

The findings derived from this rigorous analysis reveal a diverse Pareto set. This set showcases the delicate equilibrium between infrastructure investment and total annual transport costs. Notably, when budget constraints are absent, investing in ERS consistently proves advantageous. The total reductions in transport costs demonstrably surpass the initial ERS investment. For instance, the comprehensive electrification of 27,114 kilometers of highway results in a remarkable 30% reduction in total transport costs...

Amid global climate change challenges, the construction industry faces an urgent  transition from a linear production model to a Circular Economy (CE). Initiatives  and recommendations in Dutch transition roadmaps and literature predominantly  focus on ensuring a future circular built environment, while lacking concrete actions on leveraging the existing assets for reuse. Dutch CE roadmap timelines  and interventions are developed based on Material Flow Analysis (MFA) studies with highly uncertain data input, this uncertainty impacts either environment or  economy with inaccurate interventions on the CE-transition. Secondly, the Replacement & Renovation (R&R) task of civil structures poses a threat for the  industry due to the limitations of capital, contractor capacity, and material  resources required to facilitate this peak. There is currently a lack of centrally  stored high-quality physical asset data available at public organisations. This data is essential in effectively managing the decommissioning peak and reduces risk for reuse realization. Lastly, Asset Management (AM) is transitioning towards a 3D-centralised strategy in line with Building Information Modelling (BIM) and  digital twins, while existing assets are still in 2D with often incomplete and  fragmented data documentation. Consequently, a large data quality gap is forming between new and existing assets. This led to the research question: How can centrally stored, quantified, and visualised asset data of existing infrastructure impact the CE-transition, bridge R&R-task efficiency, and AM practices? An upgrade towards 3D-BIM is required for existing assets to bridge this data gap. In doing so, facilitate higher quality- and more accessible asset specific  information that can be used in reusability scanning and structural assessments,  material quantification for CE-transition roadmap accuracy, and numerous AM  benefits. The costs for upgrading the existing assets using manual modelling or  3D scanning technology are currently too large to justify. An opportunity was  identified for modelling 3D-BIM of existing beam & slab bridges from 2D drawings using a modular approach to Parametric Engineering, aiming to reduce the investment threshold, and accelerating the digitization transition. Preliminary testing executed by the author showed a potential for 50-80% reduction in modelling efforts compared to conventional modelling practices with a volume  accuracy of >97%. The prototype calls for further development, validation, and similar efforts for other infrastructure types. The tool also showed potential for 3D structural & reusability assessments, reinforcement approx., and ptioneering & circularity scoring for the design phase. To put the tool’s use in perspective, a roadmap towards 3D centralized AM and a reuse economy was developed for AM.

This thesis investigates the usability and creation of a multi-criteria optimisation tool for a facade element consisting of two outer glass layers and a 3D-printed core. The chosen constraint is related to the structural requirements laid down by the NEN codes based on the ULS and SLS. The criteria are: the transparency criterion, which maximises the transparency of the panel; the insulation criterion, which minimises the U-value; and the daylight criterion, which maximises the indirect sunlight at the summer solstice and the total sunlight during the winter solstice.

Topology optimisation is the first considered optimisation algorithm that allows for complete freedom of the core design through discritisation of the core volume with finite elements. Nevertheless, topology optimisation is not considered usable in this tool as a result of the inability to add relevant criteria to topology optimisation integrated into software, the complexity of developing an optimisation tool with topology optimisation in Python or Matlab, and the intricate optimal core designs, which are not easily printable.

Next, a hexagon structure is used as the core design with some related variables, and it is created in Rhino 7. The daylight criterion is performed with the Ladybug library, and a mesh sensitivity study is conducted. The structural constraint uses Karamba3D, and Karamba3D’s accuracy is investigated with validation with Diana and stiffness experiments. The experiments revealed a large discrepancy between the stiffness observed in the experiments and the predicted stiffness in the Karamba3D and Diana models. The insulation and transparency criteria are added, and the genetic algorithm is used to determine the near-optimal solution. Finally, a case study based on the Echo building is used to compare the near-optimal sandwich panels with regular windows. Although the computational demand inhibits quick results, the sandwich panel performs well compared to the original window in relation to the three criteria.

To achieve the goals set by the "European Green Deal" of reducing greenhouse gas emissions, it is imperative for heavy goods vehicles to transition towards becoming climate neutral. To address this challenge, Electric Road Systems (ERS) with overhead conductive and in-road inductive technology present a promising sustainable mobility solution. However, little is known about the real-life feasibility of these technologies, particularly as in-road inductive systems are partly still under development. ERS is a system of systems, it contains several linked subsystems with complex interaction effects, which makes it difficult to predict its performance across different dimensions that are important for the many stakeholders involved.
Therefore, this study aims to evaluate, from a systemic perspective, the different impacts that the two ERS technologies will have on all stakeholder groups, thereby providing input to the decision-making process on the adoption of either technology. The methodology is an adaptation of Multi-Actor Multi-Criteria Analysis (MAMCA) and Design for Value (DfV), based on a literature review, stakeholder interviews and conceptual modelling. With the study area of the corridor between Rotterdam and Antwerp, related stakeholders are interviewed to understand their main values and criteria for evaluation. The data on ERS technologies is collected to carry out the evaluation and comparison. The results of the impacts of two ERS technologies are shared with stakeholders for validation purposes.

Construction schedule optimisation problems have been explored extensively, including activity sequencing rules and work packaging. Yet knowledge is still lacking in the sequencing of mechanical, electrical and plumbing (MEP) components with geometric complexity, and how to handle conflicting precedence between MEP work packages arising from the geometric complexity. Another concept of interest is working zones, which are spaces a building may be divided into to enable parallel working: they have the potential to reduce idle working space and project duration, but its integration with the scheduling of MEP systems and effect on schedule optimisation are under-investigated. This work studies the optimisation of construction schedules for building structural and MEP systems considering working zones. First, a conceptual framework is developed, on: (1) activity sequencing rules, featuring preferences on spatial proximity and component size for MEP components; (2) clustering and cluster-splitting method, to resolve the conflicts among MEP packages; and (3) mathematical formulation of schedule optimisation problems as mixed-integer linear programming (MILP) problems. Next, a software tool consisting of an Excel add-in, a MATLAB executable programme and an Excel macro is developed to implement the framework. Two case studies are carried out. The results of case studies and further analysis demonstrate the large potential of zones in reducing project duration, the effect of the amount of resource available, and strategies for future scheduling practices. Applicability to general construction projects, limitations and future directions are also discussed.

The aim of this research was to develop a user-friendly tool for the construction industry that contributes to the decision-making of making first choices towards circular building in the initial phase of project and helps in considering the factors that play a role when reusing elements and reducing the usage of primary raw materials. With changing climate conditions and awareness of sustainable working, there has been a change in market demand to prevent raw materials from being exhausted and the living environment being affected. The current traditional industry is building with primary raw materials and is being challenged towards sustainable construction, but how the transition from traditional construction to sustainable construction will take place, is rather unclear. This resulted in the need for a good overview of different factors and aspects involved in the transition from traditional building towards circular building. A good overview was still missing and more research was needed into the innovation of reusing elements. Furthermore, the current calculation tools did not provide an insight into the total transition process from traditional building to circular building. Thus, for the construction sector, who currently has to minimize the usage of primary raw materials and reuse as much as possible, it was not clear which factors play a role in the transition process towards circular construction. Therefore, an overview of all factors involved in the transition process was necessary in the initial design phase of a project in order to be able to make a choice in the between design alternatives that meet the requirements drawn up by the project....

The offshore wind market is developing fast due to climate change. To ful fil in the growing demand for offshore wind market, one has to look for floating offshore wind solutions as nearshore shallow waters are depleting. Several types of floating wind structures can be distinguished in the following categories; Spar, semi-submersible and Tension Leg Platform (TLP) structures. Blue water developed a floating wind TLP, the BLUE-STAR, which is still under development and has not yet been applied in offshore wind projects.At the moment there are many factors unknown about the concept. This in comparison to semi-submersible structures of which more knowledge is available. The aim of the thesis is to describe if the TLP concept has an advantage over the semi-submersible structure and if the newly developed TLP concept can be a viable solution. For this, a simulation model to simulate the logistics of both structures, is developed. First, different types of offshore wind turbines are classified, including TLP and semi-submersible structures. This is followed by challenges the logistics of offshore wind farms are currently confronted with. This points out that the most important challenges are due to substructures, environmental conditions and T&I. Next, the logistic process of both the TLP and SSB structure are described. This is followed by a literature study which is divided into literature on weather conditions and literature on operations and logistics, in which the analytical approach and simulation-based methods are described.Discrete Event Simulation is used for the simulation model in Matlab. This is followed by an extensive description of the logistic process in general. The second part of the chapter elaborates on implementation of both structures into the described model. The fourth part of this thesis, elaborates on implementation of the simulation model by explaining the decisions and assumptions made for the simulation model. Furthermore, this part also discusses the inputs of the logistic process simulation.  The weather data provided by Blue water for the use of the simulation model is presented and this part of the thesis gives an evaluation of the simulation model. Based on this evaluation, is it concluded the simulation model functions correctly. In the final part of this thesis, the results of the logistic simulations of both structures are compared and a  sensitivity analysis is performed. For the sensitivity analysis, 4 cases are studied, these are: varying wind speed and wave height, variation of transport duration, seasonality and varying team performance. The aim of this sensitivity analysis is to study the influences of different input conditions.  Finally, results of the simulations and performed sensitivity analysis indicate that the newly developed TLP concept is not a realistic alternative compared to the SSB structure.    

Many firms are occupied with determining the optimal replacement time of machinery. Machine replacement is a complex investment decision that requires the estimation of future cash flows and other parameters. The non-deterministic character of future cash flows has given rise to stochastic models, that take into account this uncertainty. This study has applied a theoretical stochastic asset replacement model in practice. It was found that the stochastic replacement model can be used on real data by performing a weighted least squares (WLS) regression. Decision-makers should however be aware of the model assumptions and limitations of the model. The replacement decision-making process can be automated using a Python script that is provided in this study. However, the CMMS that was used in the case study needs to be upgraded to have additional features. When one wants to perform an analysis of assets on a system level, the expected replacement year value can be used. Until now, the probability distribution of the expected replacement year had to be computed by means of Monte Carlo simulation. In this report, a closed form solution is used for the expected replacement year distribution when operating cost follows a geometric Brownian motion (GBM). With this contribution, decision-makers in engineering asset management now have the opportunity to rapidly analyse and perform probabilistic computations on the expected economic life of deteriorating machinery on system levels, such as geographic systems or weather systems. As only few studies on stochastic asset replacement are empirical, a second important contribution of this study is the application of the model in a case study. The case study concerns HVAC systems of petrol stations in the Netherlands. The paper describes how to perform a weighted least squares (WLS) regression so that model parameters can be easily estimated for real cases. Finally, several new insights and barriers on implementing theoretical models in practice are introduced.