Currently, Ordinary Portland cement (OPC) concrete accounts for the world's 8-10% anthropogenic carbon/ greenhouse (GHG) emissions, where 90-95% of the emissions are due to the production of OPC. A 200% increase in OPC demand is projected by 2050 from 2010 levels. Therefore, it is urgent to reduce these emissions arising from its production. Alkali-activated concrete (AAC) has gained significant attention from researchers worldwide for being a sustainable alternative to OPC concrete. AAC completely substitutes Portland cement with industrial by-products, such as BFS (blast furnace slag) and FA (fly ash). An alkaline activator is used to activate the BFS or FA to form a hardened binder. Despite comparable or even better technical performance than OPC concrete, worldwide usage of AAC is not yet observed. The challenges for the commercial scalability of AAC have rather increased with the onset of coal phase-out law in many parts of the world. This limits the availability of FA and thus forcing researchers to find alternative binders to make AAC, such as using BFS as the sole binder. Therefore, while other technologies develop and blast furnaces are phased-out, it is essential to effectively use the available BFS from the steel industries to produce alkali-activated slag concrete (AASC). Although various studies proved that AASC has significantly higher compressive strength than OPC concrete, numerous researchers have reported poor workability and rapid-setting. AASC also has higher drying & autogenous shrinkage. However, despite having a higher autogenous shrinkage, AASC shows moderate-low cracking potential compared to OPC concrete. A self-compacting AASC is expected to follow the same behaviour. However, very few studies have focused on the self-compacting behaviour of AASC, which, therefore, limits AASC for various structural applications where an SCC is required. In order to upscale the commercialization, a comprehensive study is needed that provides detailed insights on the development of a SCAASC (without FA) with an appropriate mixture-design and optimization procedure, desirable fresh properties, hardened properties, sustainability analysis and commercial viability. Hence, the main research question is: Is it possible to develop a self-compacting alkali-activated concrete made from 100% BFS as a binder and currently available raw materials without using any FA? If so, What is the optimal mixture design that results in desirable fresh and hardened properties suitable for real structural applications? Is the developed concrete sustainable and ready for commercialization? And if not, what are the commercial barriers? Attempts are made to answer these questions through various laboratory experiments and stakeholder interviews. The final mixture design is developed in a 5 step optimization process. It involves studying the effect of a particle packing based mixture design method, curing temperature, silica modulus (Ms) content and different admixtures on the workability and compressive strength (1, 7 and 28 days) development of the SCAASC. In this process, an optimal mixture design with desired workability and strength properties is developed and an optimal curing and mixing regime is also suggested. This developed SCAASC is further tested for all the SCC like fresh properties. Various hardened properties such as compressive and splitting tensile strength, elastic modulus, Poisson’s ratio, bond strength to reinforcement, free drying and autogenous shrinkage are also investigated at different ages for different curing regimes. The cracking potential is also investigated for the first time using a TSTM (Temperature stress testing machine). Furthermore, the environmental footprint and material costs are evaluated through an LCA (life-cycle analysis). Lastly, stakeholder interviews were conducted with an international and local construction company to validate the developed SCAASC and exploit its commercial viability. From the experiment results and conclusions drawn from the interviews, the developed SCAASC is commercially scalable for its desirable fresh and hardened properties. It can be used in various industrial applications where an SCC is used. The 80% less CO2 emissions from a reference OPC concrete further bolster its market acceptance. However, high market costs and lack of available standards are still the major challenges.

Marine contractors as Heerema Marine Contractors (HMC) have made decommissioning part of their core business. Being the owner of two of the largest semi-submersible crane vessels (SSCVs), the choice for single lift jacket removal is straightforward. However, jackets in the Northern North Sea have a weight and height exceeding the crane capacity of these vessels. Moreover, not all jackets have the structural integrity to be single lifted. Piece small jacket removal can be a solution but is often overlooked by the expectance of being labour-intensive and therefore too expensive. This thesis has the objective to develop a methodology for advising a jacket removal strategy based on a decision support model while comparing piece small versus single lift decommissioning. Comparing the two removal methods is done for a single case study of the Kinsale Bravo jacket, provided by HMC. The budgets of both removal methods, resulting from the case study, are used as input for the decision support model (DSM). Based on the jacket characteristics, the model uses constrains, rules of thumb and calculations to determine several possible outcomes. These outcomes can then be further optimised based on costs, duration and Co2 emissions. Based on either one of these optimisations a removal scenario can be established by the model.

Purpose - This development project is focused on the combination of project scheduling and risk mitigation for large infrastructure projects. The aim of this project is to develop a model that can automatically restructure the network schedule to enable that delay is mitigated as much as possible while the effects on multiple project criteria are minimized. This model will be developed as an alternative perspective on the original Mitigation Controller. Approach - Simulation, optimization and multi criteria evaluation are combined to reflect reality and the human goal oriented behavior during the decision making process in large infrastructure projects. Including optimization in a simulation process ensures that uncertainties and risks are reflected in optimization outcomes. Embedding a multi criteria evaluation in the optimization establishes the reflection of multiple project criteria in the model. One of the largest ongoing infrastructure projects in the Netherlands is used as a case study to validate the model and establish its practical relevance. Findings - The verification and validation on the applied case study showed the theoretical and practical feasibility of the developed model. The probability to achieve a certain project target duration increases significantly for the highest aggregated preference of the applied mitigation strategy. Originality - The proposed development proposes an alternative perspective on the Mitigation Controller as it examines the optimal network logic rather than crashing activities to minimize project delays. Not only cost, but also multiple other project criteria can be taken into account in the optimization, such as traffic hindrance or stakeholder effects. On this wise Monte Carlo Simulation (MCS) is not only combined with optimization, but also with Multi Criteria Analysis. And lastly, a Genetic Algorithm is applied to minimize the project delay for the least effects on project criteria in each run in the MCS. Future work - To improve the body of scientific knowledge on this topic, further research on how to combine the model on network restructuring and the original Mitigation Controller on activity crashing into one overall automated computer tool would be of interest. This tool can then propose the best mitigation strategies both on the structure of the network as on the lead times of activities to mitigate project delays, which would be a valuable addition for project control in the construction industry.

Why do engineers often design what people don't want? And why do people often want solutions that are not feasible? This is because the current design and decision support optimisation methodologies are one-sided and ignore or fail to capture the dynamic interaction between people's preferences (desirability) and technical assets' performance (feasibility). Furthermore, most methodologies contain fundamental problems and often cannot achieve a single best design solution. Moreover, the offshore & dredging engineering (ODE) industry can significantly benefit from multi-objective design/decision optimisation as projects become larger, more stakeholders are involved in concurrent design/decision-making, and new technologies emerge. To solve the shortcomings given the current momentum within the ODE industry, a truly integrative Open Systems Design (Odesys) methodology is proposed in this thesis. For this purpose, a new multi-objective optimisation method IMAP is introduced which is operationalised in a Python-based software tool called Preferendus, including a new inter-generational Genetic Algorithm (GA) solver. The application and added value of the Preferendus/IMAP are validated for two demonstrators within the maritime contractor Boskalis. The first validation case concerns planning and design optimisation in the development of offshore floating wind farms, focusing on scheduling and mooring design. For this purpose, an integration was made with the wind turbine simulation tool OpenFAST. The new Preferendus/IMAP significantly improved the overall tender performance by: 1) providing initial design approaches within a few hours, where currently tender teams spend days working on design alternatives that, in hindsight, were suboptimal; 2) removing tender team bias from the design process and finding design solutions that were otherwise unfairly disregarded; 3) open glass-box modelling support for concurrent design between the asset owner and the contractor. The second validation case is a decision support optimisation application of a dredging production in which multiple vessels jointly execute a dredging project. Due to different types of disturbances, these vessels often have to wait for each other, reducing the overall efficiency of the project. Current expert-based optimisation approaches are limited in their ability to adjust best-for-project. The new Preferendus/IMAP shows a clear improvement and finds solutions that significantly reduce waiting time while simultaneously achieving high production levels. Moreover, the Preferendus/IMAP outperforms single-sided optimisation on production alone by achieving similar high production levels while also improving other objectives such as CO2 emissions and vessel efficiency. Steps for further development include: 1) improving OpenFAST integration and modelling; 2) addition of fatigue loading in the anchor design; 3) improving discrete event simulation scheduling modelling; 4) improving runtime by exploring other algorithms and/or programming improvements.

Negotiations in urban development projects can take a long time, going back and forward to the designer many times, even leading to stalemate situations. Requirements can be set intuitively, without knowing what the side effects of the constraints will be for the urban development. On top of that, approval of all stakeholders is needed to be able to implement the design. An integral process of decision-making, with cooperation, open communication and considering everyone’s opinion, seems to be challenging when it comes to urban development projects. Current decision-making methods, such as Linear Programming, are lacking in terms of cooperation between parties, only finding a feasible design for a group of stakeholders and satisfying one party. Considering preferences helps overcome this problem. In this thesis, an a priori design optimization approach (PDOA) has been developed as decision support system, applied and tested on a real-life urban planning case, Waelpolder. The PDOA returns a quantitative design (Program of Requirements) for the to be developed area which is considering preferences and wishes of stakeholders a priori, before a design is generated. By basing the design on preferences of stakeholders, intuitively determined requirements are avoided and designs will be more compliant to stakeholders’ needs. The PDOA generates a design by optimizing on overall preference. So, finding a feasible solution is guaranteed, on top of that the approach is able to find a most desired group solution. The Preferendus (based on Tetra) method, where stakeholders do not make the same amount of compromises in a design, is preferred to use in the PDOA rather than the goal attainment method, where stakeholders do the same amount of compromises. This is mainly because the Preferendus returns more extreme designs and can better show the impact of adjustments in requirements in comparison to the more moderate designs generated with goal attainment. In addition, the stakeholders of Waelpolder indicate that it is realistic that one party should do more compromises than another party. Therefore, the Preferendus is better able to support decision-making and open up discussions. The PDOA allows a new direction in decision-making, where negotiations become an open glass box rather than a black box. Openness & transparency, which is one of the fundamentals of the PDOA, takes away strategic play between stakeholders. Stakeholders are forced to express what they want upfront, before any design is created. The approach supports negotiations in decision-making as it provides insights in a rapid and simple manner. The tool allows requirement modification, thereby showing the impact of certain requirements and presenting alternative possibilities. In addition, long negotiation processes where stakeholders need to get aligned are avoided because of the required openness and adaptability of the tool.One of the stakeholders within Waelpolder is willing to implement the PDOA within its organization as slip school model in urban development projects, to investigate the effects of adjusting requirements. Due to the iterative value of the approach, the stakeholders are positive about using the approach in other area developments, in an early stage.

In most of the current starting construction projects, most stakeholders use virgin materials for building elements. Considering climate change, building materials are getting scarce, it is necessary to reduce the number of virgin building materials and improve circularity in the construction industry. The problem is the absence of a middle man that could research the data of existing usable materials and connect this information to stakeholders of starting dwelling projects. This raises the research question: How can an operational model link the supply of existing building materials with the demand for new construction projects in order to reduce the use of virgin materials and thereby improve circularity in the construction industry? The goal of this research is to provide a useful tool for the construction industry to join the transition towards a circular economy. Based on three levels of scale, an operational model is developed that gives a comparison between the materials costs of existing building elements and virgin building elements for a starting dwelling project. This comparison results in insight and an overview for clients, such as municipalities or housing corporations into the costs of potential dwellings built with existing building materials. The operational tool also gives insight in whether or not reusable building materials provide a feasible business case, considering a framework based on the clients input.

Infrastructure projects in the construction industry are complex. This complexity leads to unavoidable changes in scope. When scope changes are not formalized and managed correctly, it is a risk contributing to project failure. A scope change that is not formalized is scope creep. This research looks at the causes and effects of scope creep in both literature and practice. Furthermore, a case study is conducted to recommend a course of action to anticipate scope creep and its effects.

Currently, the inland shipping industry is not sufficiently incentivised to invest in sustainable technologies in order to reduce their emissions. The industry itself wants to be more sustainable, but there is insufficient financial room to invest in alternative technologies. Besides, public authorities try to achieve a reduction using command-and-control policy instruments. Therefore, this study aims to gain insights into the relationship between policy instruments and incentivising technical alternatives. In this study, emission-reducing strategies have been identified using a mixed-integer linear programming (MILP) model based on the fleet owned by the Port of Rotterdam. As a result, this study has identified two effective strategies – environmentally differentiated port dues and environmental fees. The effectiveness of the environmentally differentiated port dues can be allocated to the degree of pollution control. The implication of an environmental fee on carbon dioxide (CO2) emissions, has led to a reduction in either greenhouse gas and pollutant emission.

With an increase of data documentation and standardization in the construction field in The Netherlands, by norms such as the NEN, there is a possibility to introduce data-driven approaches to certain areas within the construction industry. One of these is the area of budget estimation which is currently fully dependent on a cost estimating professional. Due to the need for estimations that are effective and time-efficient, especially in the primary phase of a project, the potential of introducing a data-driven approach is explored through this thesis. The main objective of this research is the development of a data-driven model, in the form of a Virtual Assistant (VA), to increase the objectivity of the estimation of maintenance budgets of civil engineering structures. From a literature study it is apparent that a fitting data-driven approach for the development of this model is the machine learning technique Decision Tree Classification (DTC). The VA model is developed using historical data, in the form of past input and past output, to train the model and therefore make predictions. Data that is used as past output for this model is a budget range which is documented as a budget class and data that is used as past input is data that is ensured to be objective and gives a description of each bridge. In this case the past input data are the characteristics of the bridge which refer mostly to the dimension of the bridge, the NEN2767, which captures the decomposition and condition of the bridge and to a lesser extent the duration of the maintenance. Through exploring past cases the machine learns rules and predicts the outcome for a new case and therefore predicts the budget range. This shows in which range the budget guess of the estimator should fall. Generally in order to develop a VA model and make it applicable for industry use it is important that an organization that uses such model aligns their data storage with the DTC methodology. This means the introduction of standardizing data in classes and the introduction of standard procedures to document the data. Only when these elements are present within the organization, the data that is used for past input and output can be regarded as objective and the VA can fulfill its function which is to verify the budget estimators guess in an objective manner.

Construction projects are notorious for going over budget. It is important for a project manager to have as much grip on the financial situation of the project as possible. In order to deal with the uncertainty that is prevalent in a construction project, a project manager should be able to steer project cost back towards budget. To provide project managers with clear insight on the mitigation strategy that would be advised to be used. An adaptation of the mitigation controller (Kammouh et al, 2021) was developed in order to provide insight into mitigating project cost, as opposed to the original focus on project planning. By adding goal focussed optimisation to a Monte Carlo Simulation, the human element of the corrective behaviour that is shown by a project manager is taken into account. This removes the fundamental modelling error that is present within traditional probabilistic approaches. Through a real-life case from an infrastructural project in the centre of the Netherlands, its usefulness was shown. The Cost Mitigation Controller identified the most effective mitigation strategy for the projects current situation. The results from this study were evaluated using an experiment that was done with practitioners that work in project management roles. As a result it was shown that the Cost Mitigation Controller outperformed all project managers that were involved in the experiment. The probability of staying within budget for the Cost Mitigation Controller (74.6%) was around 4% higher than those of the three top performing project managers (70.3%).

Purpose – One of the long-standing issues in the field of real estate management is the alignment of demand and supply. When building retrofit is initiated to comply with changing demands, the decision-maker has to deal with several, often competing, criteria. Smart systems have the potential to contribute to the process of optimally attuning the building with the changing demands, in other words, adding value to the building. To date, plenty of research exist on predicting the added value based on quantitative variables but fail to incorporate quantitative variables. The purpose of this paper is to provide a decision model that is capable of predicting the added value of smart systems in a building. It offers real estate managers a Preference Function Modelling (PFM) process, structure and model to approach a multi-criteria problem. Design/methodology/approach – The PFM approach, an evaluation operational research methodology, is designed to help decision makers to choose the most preferred alternative from a set of already existing alternatives. A decision model is made in Excel that enables the construction of measurement scales to which linear algebra and calculus are applicable. A second research technique being used is the Lagrange curve. The model is tested on a single building in the portfolio of Schiphol Real Estate. Findings – The PFM model enables the decision-maker to establish a list of overall preferences scores of all relevant alternatives. The alternative with the highest overall preference score is predicted to add the most value to the building. The pilot study reveals that the procedure resulted in a result but a certain level of uncertainty exists. The cause of the uncertainty seems to be a lack of knowledge about the alternatives. Research limitations/implications – The Lagrange curve is a suitable technique for changing values of a variable. However, the curve tends to overshoot. Practical implications – The practical applicability depends on the selection of alternatives and knowledge available. If the alternatives are not known a priori, the PFM approach cannot be used and Preference Based Design (PBD) is more appropriate. If the knowledge available is low and difficult to improve the input is subject to uncertainty. The output may not project the actual situation. Originality/value – The research is based on the PAS approach from Arkesteijn et al. (2017). The PAS approach is a PBD methodology and is adapted to a PFM methodology in order to fit the intended purpose of the research. The PFM methodology takes both scaling and the Lagrange curve into account and along with an iterative procedure the decision-making process is well supported. The model provides a custom-made outcome, which is of high value for the decision-maker in the SHG case.

Nowadays, the term "automation" is gaining extensive interest in both industrial and business fields. There is also a trend of achieving deeper automation in the business processes, which is a part of Business Process Automation (BPA). Among various BPA technologies, Robotic Process Automation (RPA) has attracted increasing research attention. RPA is an emerging form of business process automation technology. The practice of performing RPA results in the deployment of attended or unattended software agents to an organization's environment. ChatBots, as the outcome of the development of artificial intelligence (AI) technology, has been regarded as a potential and powerful tool to achieve such cognitive automation in service-oriented business processes. However, limited knowledge on the design road-map and approaches of ChatBot implementation in the Business process hinders the ChatBots further deployment into company practical operation. Herein, the research is focused on the question: How should ChatBots be implemented in service-oriented processes? Chapter Introduction contains the introduction of this study, including the background, problem statement, research question, and thesis structure. Chapter Literature Review describes the background of business process management (BPM), ChatBot technology, and the current state of ChatBot-implementation in business processes. BPM holds great potential for significant cost saving and productivity improvement, and the lifecycle of BPM serves as one of the guidelines for ChatBot-implementation methodology designed in the next chapter. Chapter Methodology Design introduces the requirements, design strategy, description approach, and validation and evaluation of designing a ChatBot implementation methodology. The requirements of methodology design are described according to the limitation of the current ChatBot-implementation methodology. Then the methodology design strategy is proposed. The methodology can be developed based on the BPM lifecycle, which ensures the business processes covered by the BPM lead to consistently positive outcomes and delivers maximum value to the organization, together with the Quark ChatBot implementation methodology. Following is the approach to describe a methodology. It is crucial to depict the methodology with clear logic. The analytical framework of Sol can be regarded as a suitable approach to describe the ChatBot implementation methodology. The framework consists of three parts namely way of thinking, working, and modeling. Methodology validation and evaluation are described at last. To check the validity and evaluate the methodology, it is necessary to use a case study to execute the methodology. By employing the method of case study, the methodology can be practically performed to automate a process by implement ChatBots. Accordingly, the validity of methodology can be examined, and the limitation of the methodology can also be explored, laying the foundation for the further improvement of the methodology. Chapter ChatBot-implementation Methodology designs the ChatBot-implementation methodology according to the design strategy and description approach in Chapter Methodology Design. It follows the analytical framework of Sol (1982). Chapter Methodology Evaluation evaluates the ChatBot-implementation methodology. The Call Management process of Philips IT Global Services was chosen as an example case to apply the ChatBot-implementation methodology to obtain an automated process. In Chapter Conclusion, conclusions are drawn. Research findings answer the previously mentioned research questions. The major contribution of this research is establishing a theoretical ChatBot-implementation methodology for service-oriented process automation. Although, there still are some limitations of the work. For further research on ChatBot-implemented process automation, efforts can be focused on the executable process model, the business process model towards the ChatBot model, the extension of methodology application, the multi-process ChatBot implementation methodology, and ChatBot-based business process management.

An improvement study to the pre-processing process of Damen Shipyards Galati (DSGa) is performed since benchmark analysis shows that the current pre-processing process underperforms, compared to industry peers. The objective is to increase DSGa’s throughput and decrease space utilisation, such that pre-processing facilities S1 and S1a can be merged into S1a and DSGa meets the industry ‘standard’. Lean Manufacturing shows the potential to enable improvement and is adopted to initiate process redesign. The Lean principles drive the approach of this study, which studies the ‘current state’, defines improvement strategies and implements the strategies in ‘future states’. Finally quantitative analysis shows that the objective is met.

Context – The stricter regulations on energy-efficient developments to prevent climate change and resource depletion, the expected rising energy prices and hence the rising monthly living costs for households, the changing process in urban area development, the demand for housing in the western part of the Netherlands and the increasing possibilities of technology. Objective – To identify effective possibilities for optimisation of the business case of a real estate developer in the realization of energy neutral residential neighbourhoods with all-electric, single-family, owner-occupied dwellings. The definition of energy neutral in this research is about net zero building-related energy demand. Methods – Literature review, expert meetings, case study research with semi-structured interviews, linear programming, expert focus group meeting and unstructured expert interviews Results – 1) Identification of various solutions within current practise to optimise the business case from a viewpoint of the real estate developer to realize energy neutral residential neighbourhoods. Aspects of the business case are split in: organizational aspects (e.g. parties involved, public-private collaboration, type of contracts), the financial aspects (e.g. method of financing, amount of investment and total costs of ownership) and the physical aspects (e.g. dwelling program, spatial and/or building technical interventions) 2) Creation of the decision support tool (DST) for modelling the optimal business case with output in the form of the optimal dwelling program and energy installations 3) Identify the optimal business case to get an energy neutral neighbourhood by applying the DST. Conclusions - All outcomes of the different research methods show that it is possible to create a feasible business case for a real estate developer in the development of the highest energy ambition of present-day, zero-on-the-meter, within current available methods and means. The business case of energy neutral residential neighbourhood development can be optimised within all its aspects. First, by integrated contracts and involvement of (energy) advisors early in the project. Second, by the involvement of third financing (e.g. Energy Service Companies) and a higher selling price, which is made possible by the additional borrowing capacity for the mortgage. Third, it is recommendable to apply a conceptual approach within the dwelling design and to the installation technology on the scale level of a single-building. In the case study and most of the modelled outcomes these concepts are based on ground source heat pumps and PV-panels. Last, the modelled output showed that BENG-legislation could lead to bigger dwellings. In addition, the designed DST has added value for the multi-actor decision-making processes in residential energy neutral neighbourhood development. The DST could help as guidance to provide insight in the various possibilities by exploring the solution/ design space during multiple decision moments and the DST could also be useful in education of laymen. Keywords - Energy neutral residential developments, BENG, zero-on-the-meter (‘nul-op-de-meter’), financing energy efficiency, Energy Service Company (ESCO), green mortgage, additional borrowing capacity, decision support tool, case study research, linear programming

Projects in the Complex Engineer-to-order (ETO) sector are subjected to frequent schedule delays caused by engineering changes, supply chain delays and fabrication delays. Project organizations are faced with the challenge of realigning the project duration within the pre-determined time. Schedule delays often lead to 3-5% rise in project cost. This requires an efficient on-the-go reactive approach. At present, the development of strategy to realign the project is extensively manual in nature. This makes the exploration of alternative realigning strategies cumbersome. Lean Project Planning (LPP) and advances in Building Information Modelling (BIM) has enriched on-the-go planning. However, no existing tool equips the project organization to generate, visualize and evaluate possible set of alternative strategies in the event of a triggered change. No attention has been provided to integrate strategy generation algorithms like Evolutionary Algorithm with the LPP and BIM approaches. In this research, a tool was developed to enable the project organizations to generate and visualize strategies integrating Modified Evolutionary Algorithm (MEA) with LPP and BIM from a metaheuristic approach. Furthermore, by undertaking a case study on strategies adopted in real-life change events in a representative Complex ETO project, the reliability of the generated strategies was investigated. The application of the proposed tool in the real-life test case showed the advantages of having multiple alternative realignment strategies.

With the rapid development of real estate market, due to trends as the change in the way of working and automation/digitalisation of working processes, flexibility is one of the most important tasks for owner/investors of office buildings to invest in. Currently, 60% of the vacant offices are structural vacant. Flexibility could be used as an instrument to prevent future vacant office space. The flexibility to change characteristics of a building during its life cycle is called the adaptive capacity of a building. Adaptability can be regarded as way to make the real estate supply more dynamic and better able to cope with demand dynamics (static supply and dynamic demand) and in this way extend the functional lifespan of buildings. The problem is that real estate investors are hardly interested in investing in office buildings to increase the adaptive capacity. Thereby, is the most used financial method the DCF method which is currently insufficiently used to simulate the future uncertainty in an investment in flexibility on the longer term. This aim of this research is to illustrate how to cope with the future uncertainty in a new financial method. By using a stochastic approach with the Decision Tree Analysis the investors could be stimulated to invest in the adaptive capacity of office buildings and increase the adaptive capacity of a building to respond if the functional demand changes.

Environmental sustainability has become an urgent matter on the Dutch political agenda, which will affect the built environment in the near future. Consequently, organizations have to adapt their real estate to new regulations for building performance and their own sustainability goals. The Preference-based Accommodation Strategy (PAS) is a decision-making strategy that aids organizations in finding a Corporate Real Estate Portfolio (CREP) that aligns with the organization’s values. This research aimed to stimulate organizations to improve the environmental sustainability of their CREP by changing the decision-making process. Therefore a sustainability reference model (SuRMo) was developed for PAS and tested on the CREP of Colliers, an international real estate consultancy firm with multiple offices in the Netherlands. In the pilot study, PAS and the SuRMo were used to evaluate three alternatives for a new office space in Utrecht. In an iterative process using the operation research methodology four tests were conducted which compared the outcome of the different decision-making processes. The four tests analysed 1) the current decision-making process, 2) the decision-making process and outcome using PAS, 3) the decision-making process and outcome using PAS with the SuRMo 2.0 and 4) the sustainability performance of Test 1-3 compared to Dutch sustainability goals for 2050. In the three tests the decision outcome resulted in the same office building that performed best in terms of environmental sustainability and matches the governmental goals for 2050. However, between Test 1 and Test 2 the total number of criteria increased from 7 to 37 and from two implicit environmental sustainability criteria to five explicit criteria. The outcome of this research shows that PAS increased the number of environmental sustainability criteria and changed the decision-making process of Colliers from implicit to explicit. The stakeholders expressed the need for the SuRMo because they lack knowledge about environmental sustainability in CREP but concluded that it requires further development for user-friendliness and suitability with PAS. The three actionable conclusions for practise are that PAS should be used in decision-making about CRE with a further developed SuRMo, Green Building Rating Systems should be used for sustainability in CRE decision-making instead of greenhouse gas emissions and project developers and investors can use the explicit outcome of PAS to adapt the supply to the demand on the real estate market.

Research on the influence of political and economic uncertainty on the decision-factors for accommodation decision-making in corporate real estate management. Research showed that economic uncertainty, and not political uncertainty, did influence the decision criteria during the last decade. A shift can be seen from practical, costs and efficiency related criteria to accommodation criteria that are focused on branding of the building and the attraction the best employees in these times of war-for-talent. The aftermath of the financial crisis caused this change, since the current upcoming economy creates an upcoming market with more and new business opportunities. Companies use their real estate to differentiate from other companies and to attract and retain the best employees.

In response to the European Commission's initiative to shift freight transport from road to inland waterways, there is a need to expand the capacity of inland ports to accommodate larger cargo ships. This expansion requires lowering the water bottom, which will increase the forces exerted on the quay walls. The quay walls should therefore be reinforced by applying structural adjustments, for minimal costs and environmental impact. The goal of this thesis is therefore to develop a decision support tool, which is able to directly determine the preferred configuration of structural adjustments. To maximise the aggregated preference of all stakeholders involved while satisfying the systems constraints, the a-priori design optimisation method of the Preferendus is applied. The tool, developed using computational science, uses Python because of its capability to automate repetitive calculations. It integrates with the program D-Sheet Piling that is used for specific sheet pile calculations. The tool is designed to be used during the preliminary design phase, enabling quick assessment of potential reinforcement adjustments and facilitating insight into the preferred solution. It evaluates three main strategies for quay wall reinforcement: lowering active soil stress, increasing passive soil stress, and enhancing pile stability. The tool uniquely focuses on maximising the aggregated preference of involved stakeholders and is capable of evaluating failure mechanisms of sheet piles. It is tested on three case studies, all located in the industrial harbour Loven in Tilburg. The results show that the tool effectively proposes which structural adjustments are applicable to create a sheet pile design that satisfies. The thesis concludes by drawing specific conclusions for each structural adjustment considered in the project. Moreover, it concludes that the development of a decision support tool has been successful. In particular, the tool enhances efficiency in sheet pile calculations, offers detailed insights into the environmental and financial impact of adjustments and enables the direct determination of the preferred configuration of structural adjustments. This eliminates the need to choose the preferred configuration from a number of designed variants, which is the current approach. Additionally, the thesis recommends to conduct a follow-up research on the applicability of underwater anchors, which have shown significant structural potential. Furthermore, it recommends to conduct laboratory tests to potentially improve the cohesion and angle of internal friction of soil layers. If those soil parameters can be improved, no structural adjustment may be necessary to reinforce quay walls at all.

The growing need for sustainability and circularity in the construction industry has given rise to the demand for proper sustainability valorisation and evaluation methods. Investors and project owners need sound rationale for their returns on investment made into sustainability – in financial, environmental and social benefits achieved. Promoting sustainable building methods and the use of sustainable products and materials demands a tool that can justify the investment into them. This thesis addresses this challenge and provides a decision making tool that can evaluate and compare the true lifecycle values of construction projects, in order to allow investors and project owners to compare between design options and select the most optimum choice based on their preferences. Thus the model gives a direct relation between the investment and total returns, and acts as a guide for investors on the selection of design and material choices for the needed level of sustainability in their projects. Termed as the “Total Value for Society” model, it uses Multi-criteria Decision Analysis methodology to pick the construction project with the most “true lifecycle value” out of multiple options of the same functional equivalence. The tool is applied to a test case provided by Heijmans. The results of this model are verified and validated through stakeholder interviews. A sensitivity analysis confirms the robustness of the model. An expert review performed using stakeholder interviews validates the usefulness of the model and its impact on the design selection process. The validation shows that the model’s usefulness to owners/investors, contractors and designers indeed matches with their expected uses of the model and fulfills its needs as established by the same stakeholders earlier. Finally, the report concludes by answering to the main development statement - The Total Value for Society Model, by comparing design options based on their lifecycle performances on financial, environmental and social sustainability aspects, and showing the most optimum option for the given preferences on the relative weights of criteria, can enable investors to evaluate and compare the true lifecycle values of construction projects, and doing so, guide their investment decisions. The thesis however is just one step towards rationalising investment decisions regarding sustainable construction, and it further provides new avenues for future research and developments.