The purpose of this graduation thesis entitled "Probabilistic restructuring of complex construction project activity linking using GERT: an alternate network distribution in mitigation controller" is to analyse the effect of project network structures when the duration of the project activities is distributed stochastically and its following effect on the existing Mitigation Controller©. The researchers at TU Delft have developed a state-of-art tool called Mitigation Controller© (MitC) for automating the search for finding the most cost-effective set of mitigation measures to ensure the probability of the project's completion at a required level. However, there is a modelling error in the project network diagram of this tool. The risks and uncertainties are modelled using a Program Evaluation and Review Technique (PERT) distribution. The current Mitigation Controller has managed
to recreate the human-oriented selection of mitigation measures, but it has not considered all possible scenarios within a complex construction project.

PERT is the most straightforward distribution used for explanatory purposes. This does not reflect reallife conditions. There are instances where certain activities are repeated when the result does not meet the required quality. To overcome the limitations of PERT, it is suggested to implement the Graphical Evaluation and Review Technique (GERT) for scheduling construction projects. A new code
is integrated into the existing Mitigation Controller to generate new network paths based on the probabilistic nature of the project activity. The novel network structure is used to compute the optimal set of mitigation measures using Monte Carlo analysis and linear optimisation. It is also observed that the optimisation solver takes a substantial amount of time to compute depending on the number of activities on the project. An efficient Monte Carlo analysis is implemented to reduce optimisation time within the tool.

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.

Innovative solutions for safety and risk management on construction sites are required to reduce the amount of accidents that occur globally, as too many occupational accidents still happen in the Architecture, Engineering and Construction industry. A particular problem is the fall from height (FFH) accidents on construction sites, due to failing barriers with the underlying cause of insufficient planning. In previous research it has been suggested to develop dedicated BIM plug-ins to automate and visualise risk identification and evaluation of construction sites as a means to assist the safety management process. To explore the impact of BIM on FFH accident reduction through automation and visualisation, a digital tool prototype is developed. This prototype focusses on fall from height (FFH) identification on construction sites during early project phases of civil engineering projects. It is programmed in Autodesk Dynamo for Revit, based on technical and functional requirements derived from literature and industry professionals. A simulation of the FFH tool prototype has been conducted through a pilot project. The result of this product development is a working fall from height detection prototype that is to be used as a supporting tool during the safety analysis of construction site design in Dutch civil engineering projects. The developed tool is added to the body of products that can be used for digitalisation and innovation within the construction process, where it digitalises part of the safety management process that is otherwise performed manually. The added value of the tool prototype is the addition of automated risk detection, creating support in the design and planning process and providing added information to group discussion on safety matters in risk identification and evaluation meetings. Recommendation for future use of the tool is to implement the FFH tool prototype during the design phase to provide the designer with insights in the safety of the constructability. Additionally, the FFH tool prototype can be implemented by safety managers to use the results during safety meetings for better discussion and evaluation of the construction site. For the implementation, improvement of the level of detail in the 3D models for the projects and including temporary construction site works is essential. For further development it is recommended to focus on improving the import of linked models into the script and developing alternative operations to determine the height differences. More developments and improvements can be made to the script to increase the applicability on more complex projects. Concluding, the FFH tool prototype is considered to bring added value in digitalising an otherwise manual process regarding safety management. It is suggested to incorporate use of the FFH tool prototype in the design and planning process to proactively engage in digitalising and innovating engineering processes.

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.

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.

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.

In general, costs are an important aspect long before the construction of a civil project starts. In the conceptual phase, which is the first phase of a civil construction project, little cost information is available, and the cost estimate is at that stage of project development less accurate than when the project is almost completed. When a cost estimate is made with a conventional method, such as SSK, in the conceptual phase of a civil construction project, the Association for the Advancement of Cost Engineering, AACE, argues that the error of a cost estimate compared to the real price can be 15-50%. The lower accuracy in the conceptual phase is caused by several obstacles: a lack of estimating experience, lack of information, and a method that cannot calculate an accurate cost estimate. Besides, the conventional approach is time-consuming. Machine learning models can overcome these obstacles. This research is initiated by a practical problem within the company Witteveen+Bos. Witteveen+Bos uses a conventional approach for cost estimates and wants to improve the accuracy and calculation time of cost estimates of road bridges in the conceptual phase of a project. This research aims to improve the cost estimates of bridges in the conceptual phase of bridge projects by use of the machine learning model called FALCON. This model is already successful applied in the field of cost estimates for construction project types. Improvement that should be realized in the reduction of the calculation time from hours to minutes. Besides the research aims to reduce the maximum deviation of estimates for road bridge projects to 30%. The FALCON model is not the only model to solve an estimation problem. Standard models, suitable for estimation problems, are multiple linear regression, K nearest neighbors (KNN), and decision trees. The results show that the average absolute deviation is 34% for the KNN model, 36% for the decision tree regression model, 57% for the multiple linear regression model, and 24% for the FALCON model. All 4 models are able to calculate results within a couple of minutes. FALCON is the most accurate model The error of the individual predictions of the FALCON model roughly corresponds to the bandwidth of the AACE (15-50%) for the conceptual phase of a project. The validation of the FALCON model is realized through interviews with cost estimators of Witteveen+Bos. These interviews showed that the realized results meet the expectations of the interviewees. Besides, the interviewees are willing to implement the model in practice. In the end, it must be concluded that the FALCON model can calculate a cost estimate, of bridge projects in the conceptual phase, more quickly, and with a comparable accuracy level as with conventional methods that are used today. For future research, it is recommended to use a larger dataset that has less variation. The used dataset consists of 39 bridge projects. Besides, it is recommended to use international standards for data structuring instead of national standards.

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%).

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.

Infrastructures have become important for the holistic growth of any country. With the number of such projects being constantly on the rise each year, its efficient maintenance has become a primary requirement for any asset owner. Among the many techniques available, FMECA is widely used for maintenance planning. In this method, different critical components are identified based on their likelihood of failure and impact on the infrastructure. Based on the inferences obtained, maintenance plans are developed where interventions are prioritized based on a component’s criticality within the system. FMECA uses risk matrices to assess criticalities semi-quantitatively. This lacks the required quantifiable information of a component in a system. A component by design can either be individual or exist as a subsystem comprising several components. To analyze criticalities accurately, quantitative information that can provide insights on a component’s state in the system is required. Hence, this thesis develops a new approach that could eliminate subjective errors in conventional criticality analysis. The new and conventional approach was applied to two infrastructures to observe and discuss the variations in results obtained. The comparative study showcased that the new approach provided better quantitative reasonings for criticalities obtained for each component. Using the unique implementation of the obtained quantitative data as done in the current research, developing realistic and accurate maintenance strategies is possible.

This thesis ‘A decision support model for analysing the reuse potential of hollow-core slab floor components’ describes the development of a decision support model to investigate the reuse potential of a structural building component. For this research, the hollow-core slab floor component is chosen as the structural building component to be reused. The main research question to be answered is: ‘How can various methods and knowledge be integrated into a decision support model for deciding on the reuse potential of a hollow-core slab floor component to close the material cycle?’ The ambition of the Dutch Government is to transform from a linear economy to a circular economy, whereby waste is excluded and products will remain in the system for longer at a high-level value. However, the barrier preventing the implementation of high-value reuse is the lack of knowledge to understand the opportunities and the impact of high-value reuse. It appears that the reuse of hollow-core slab floor components is not yet common because of the uncertainty and risks involved in the reuse of these components. The component has various properties that are advantageous for the reuse of these components, such as the lightweight of the component, the long technical lifespan and the standard dimensions of the component to use it all over the world. By way of contrast, a concrete topping, bonded connections, and the high costs for the deconstruction of the components are seen as barriers to reuse the components. However, it has been investigated that it is possible to use these floor components for longer than they are currently being used. Still, this is only possible if there is a full understanding of the reversibility, the reuse options, and how to increase the reuse potential of these floor components. The ‘reuse potential factor’ is a factor between 0 -“waste-like”- and 1.0 -“resource -like”- and it is defined as a factor measuring the ability of a construction component to retain its functionality after the end of its primary life related to the economic and environmental impact of reusing this component. Therefore, the factors related to the quality – qualification factors – should be tested and the factors related to the economic and environmental impact – quantification factors – should be determined.
There is a demand for a model that tests the performance of building components to assess the reuse potential of building components at the end-of-life stage. It is about making a decision about the consideration of the use of a new manufactured structural building component or a reused structural building component where the impact on the environment and economy remains limited. The first step to measure the reuse potential factor of the floor component is the reuse analysis in which the quality of the reused component should be determined based on the qualification factors. The qualification factors to be tested are the lifespan performance, technical performance, functional performance, aesthetical performance, and additional performance. The second step is the reuse evaluation where the ‘costs’ of the reused component are calculated based on the quantification factors. The quantification factors are the environmental impact costs and the economic impact costs based on the LCA and LCC tool. The decision support model analyses the relationship between the qualification and quantification factors of the reused hollow-core slab floor component and to compare the outcomes with the factors of a new manufactured hollow-core slab floor component. The qualification factors of the existing floor component are tested to see if they meet the required performance of the new construction project, or if the component must be adjusted. This will influence the ‘costs’ and the reuse potential factor. This model can be a tool that positively contributes to the ambition of the ambition of the Dutch Government about the transition to a circular economy.  

This research attempts to automate a portion of the design process for construction projects that follow Systems Engineering. Systems Engineering divides each stage into three main processes. They are the definition of requirements, functional analysis and allocation and design synthesis. This research attempts to automate the functional analysis and allocation because this process is repetitive, laborious, and time-consuming. The proposed system uses natural language processing to analyse the requirements and allocate them to objects in object type libraries (OTL). The OTL that is used in this research uses the OTL developed by Rijkswaterstaat. This OTL contains typical objects and sub-objects that an asset should include along with its metadata. This metadata constitutes a description of the object. The proposed system uses a dependency parser to add syntactic information to the requirements and object description. The requirements and objects are then subjected to Noun Chunking. Noun Chunking is an operation that breaks down the requirements and object description into meaningful phrases. Next, these chunks are compared for semantic similarity by plugging it into a word embedding to allocate each chunk a vector. The similarity is measured by finding the dot product of the two vectors. This system is converted into an NLP pipeline that identifies the correct requirement chunks to object chunk. The system is then compared to a neural classifier. Verification and validation are performed by measuring precision and recall, along with semi-structured interviews with industry experts. This research concludes that the proposed system can use process improvements. Still, the word embedding and cosine similarity is the suitable technology for the automating the allocation of requirements to object libraries.

Asset degradation and maintenance strategies are driving factors that cause the variation in life cycle cost (LCC) of infrastructure assets. In the railway industry, several researchers have analyzed the impact of the uncertainty on LCC, but the emphasis was laid on traditional ballasted tracks. Another type of track structure, embedded rail system (ERS), has not received much attention to date. The research screens the problem to Harmelen level crossing system (LCS), one of the applications of ERS, and proposes a reliability-based LCC model for this type of railway assets, which incorporates the uncertainty associated with rail degradation and maintenance interventions to LCC analysis in order to optimize the maintenance strategies and achieve a cost-effective life cycle management of Harmelen LCS. The model application is demonstrated in Microsoft Excel and partly validated by field data. With the rail failure data, cost information and pre-defined maintenance policies (3 conditions), the model output is able to answer two practical questions for asset managers: ‘when would be the most cost efficient to preventively replace the rails in Harmelen LCS’ and ‘how much money does one asset manager have to save to own the asset in the optimized lifecycle’. Sensitivity analysis is further performed to take more different settings into account and facilitate better understandings about the connections between model outcomes and input variables. Recommendations concerning the model application are provided both for Harmelen LCS and other types of railway assets.

Water Treatment Plants (WTPs) play a key role in assuring water availability to citizens and businesses. Long term availability of these plants is the interest of the asset manager of a WTP and can be influenced by maintenance strategies and modifications of the system. A WTP is characterized by a complex configuration and repairable, deteriorating assets. Currently, no method in the literature is known for modelling the long term availability of systems with these characteristics. This research introduces a two level hierarchical model: it deals with complexity at the system level and with repairable, deteriorating assets at the component level. At the component level the asset manager has to select between a condition based or failure event based model. A flowchart supports the decision making process for this choice. The result is a method which allows the asset manager to (1) learn about what key components affect long term availability of a WTP and (2) explore maintenance strategies and modifications to the WTP and their effect on long term availability.

Cables and pipes are critical infrastructure systems (CISs) which are mostly located in the very crowded subsurface. Especially in urban areas, a typical road includes five to ten infrastructure systems which are all owned and managed by different entities. The CISs are spatially interdependent as these are highly interconnected due to the close spatial proximity. Despite the critical function of cables and pipes, over 30,000 cable and pipe failures from excavation works are reported in the Netherlands yearly. Multiple studies have been conducted to reduce the risk of excavation damage. These studies have mainly focused on the impact side. Remarkable as from an extensive cooperation between the network operators and other stakeholders, a guideline (CROW500) was formed that seeks to prevent cable and pipe damage from excavation works.
The objective of this thesis is to develop a model to accurately predict cable and pipe failures from excavation works, considering spatial interdependencies. The associated research question is: “What method can predict the influence of spatial interdependencies on the probability of failure from excavation works on the cables and pipes of subsurface utility operators?”
Predictive analyses are frequently used for enhanced decision making by subsurface utility operators, whereby cable and pipe failures, having large impact in this sector, are relatively rare. This thesis explores the possibilities of modelling rare event data within the subsurface utility industry, through which specific situations, such as failures from excavation works, can be considered. A case study within Evides Water Company was conducted whereby 107,000 non-failures and 180 failures from excavation works were collected. In terms of statistical techniques, alternative models to logistic regression and Bayesian logistic are considered. Two approaches, involving weighting and synthetic minority oversampling have been examined to compensate the imbalanced classifiers in the data set. Balancing is done by over-, under sampling, as well as by weighting, which aim to increase the accuracy of the models. At algorithm level, under sampling and weighting combined were tested and found to improve the balanced accuracy to 0.66 with 0.38 of the failures predicted. At data level, over and under sampling by SMOTE resulted in 0.58 of failures predicted and a balanced accuracy of 0.61. These results proved that logistic regression for network operators can predict failures in specific situations with reasonable accuracy.

The municipality of Amsterdam is facing the challenge to deal with uncertainties accompanied with the maintenance planning of maintenance activities on bridges. Long term asset planning (LTAP) has the goal to show the proposed expenditures, capital and operational expenditures (CAPEX and OPEX), for the long term to be able to build up a monetary buffer to absorb the CAPEX which is necessary when a period of large expenditures due to multiple major overhauls and replacements comes up. LTAP also gives the opportunity to program the more expensive activities which create the advantage of spreading expenses over a longer term. The Long Term Asset Planning of Amsterdam makes use of a deterministic time value for activities on bridges, while in fact this value does not give a reliable image of the actual situation. The majority of maintenance activities seems to be performed after the deterministic time value has been reached. Thus, the long term asset planning copes with uncertainties which results in an unreliable outcome and prospect in the expenditures for maintenance. Uncertainties can partly be solved by the use of deterioration models. The parameters involved that predict the physical behavior are mostly an assumption and not known with certainty. Data can be derived from historical maintenance data. A problem faced by all municipalities in the Netherlands is the absence of reliable registered quantitative data. Even the presence of past data would not guarantee its future reliability due to the dynamic characteristic of an urban environment. A method has to be found to retrieve the necessary data alternatively to improve the reliability concerning maintenance intervals for bridges in order to improve the reliability of long term asset planning for municipalities like Amsterdam. Expert judgement is a method that has proven to be a mature and scientific tool for achieving the necessary data that is unavailable at this moment and will be used for this research to develop probabilistic intervals for maintenance activities. By qualifying and quantifying the uncertainties regarding the maintenance on bridges, improvements may be realized on the short- and long term for the costs in maintenance planning. A questionnaire reveals that experts in the field have trouble assessing condition over time. A recommendation is given to solve this issue in the future.

neering (SE). Nowadays, this approach is more and more instructed by clients in the Netherlands. The switch to Systems Engineering was necessary because of the development of integrated contracts (UAV-GC; Uniform Administrative Conditions for Integrated Contracts) such as Design-Build-Finance-Maintain (DBFM) and Design & Construct (D&C). Before the introduction of integrated contracts, mainly RAW contracts (Rationalization and automation Soil, Water and Road Construction) were used in the Netherlands for public procurement in infrastructure projects. In RAW contracts, the to be built system is described in detail and the segregation of responsibilities between client and contractor is distinctly defined. In integrated contracts, this distinctly defined segregation of responsibilities is missing. As a result of the integrated contracts, the projects became more complex and multidisciplinary, which asked for a more structured approach (Emes et al., 2012). Another difficulty of integrated contracts is the communication between client and contractor because more information has to be transferred. An important element of SE are the verification and validation processes. This validation process is considered complicated in the civil engineering industry. Four subproblems are determined by means of literature and practical knowledge. First, the interaction between client and contractor. Findings of a questionnaire performed by the CROW (2017) about the relationship between client and contractor are:56.9% of the contractors’ employees do not feel equally treated by the client, 43.6% of the clients’ employees and 69.7% of the contractors’ employees agree on the statement that the other party is mainly defensive instead of cooperative, another issue during the interaction between client and contractor is trust. 23.3% of the clients’ employees and 17.7% of the contractors’ employees say that the other party cannot be trusted, 73.6% of the clients’ employees and 73.5% of the contractors’ employees disagree on the fact that the other party puts the common interests of the project above their own interests. The second subproblem is the confusion between verification and validation. Verification is the process of determining if the system meets the specified requirements (Alsem et al., 2013). Nevertheless, the purpose of the validation process according to ISO (2015) is "to provide objective evidence that the system, when in use, fulfils its business or mission objectives and stakeholder requirements, achieving its intended use in its intended operational environment" (ISO, 2015, p. 74). The third subproblem is executing sufficient validation. The difficulty is to represent the client and stakeholders needs in requirements. Therefore, the validation is often conducted last-minute in the construction phase of the project. The fourth subproblem is that the effects of the use of 3D visualisation during design validation on project performances are unknown. These problems result in the following research objective and question. The aim of this research is to gain insight in the effects of 3D visualisation during the design validation on the project performances, namely time, costs and quality. What are possible effects of 3D visualisation on design validation within the civil engineering sector in the Netherlands? The methodology for this research consists of three phases: (1) context, which includes literature and project evaluations, (2) experiment, which contains baseline and follow-up measurements and (3) the results and conclusion. Phase two, experiment, is a gaming simulation called the ontwikkelstraat. In this research, the following definition is used for gaming simulation: "a special type of model that uses gaming techniques to model and simulate a system. A gaming simulation is an operating model of a real-life system in which actors in roles partially recreate the behaviour of the system (Meijer, 2009)". The Ontwikkelstraat is a two-day case imitating a project from tender till exploitation and includes design, construction and maintenance of a hydrogen factory. During this gaming simulation, three types of measurements are conducted: questionnaires, recordings of the interaction between client and contractor, and observations. According to the literature, sufficient design validation has to fulfil several conditions. The aim is to acquire proof that the system in its ability to achieve its intended use (ISO, 2015). Rijkswaterstaat (2017) prescribes three conditions the design validation should meet, namely design validation is an integral element of the design process, confirmation by client and stakeholders, and determine the validation process in cooperation with the client. There are multiple ways of conducting design validation. Several design activities are of importance for design validation. During the system design phase, these activities are analysing contract documents, as well as document functionalities, requirements and systems. In the preliminary design phase, these activities are based on the variants and validation of the design products. It is concluded from the three cases studied that alignment of the design products with client and stakeholders is a key validation step in all cases. Bryde et al. (2014), Sebastian and Van Berlo (2011), and Azhar (2012) described the capabilities of 3D visualisation. Three-dimensional visualisation can be static or dynamic. With a static image of the 3D model of the design, the communication and coordination between actors and disciplines are more detailed. By decomposition in subsystems and objects, the technical details of the design can be sufficiently discussed during the validation sessions with the client and stakeholders. Another capability of the 3D visualisation is expectation management between contractor, client and stakeholders. 3D visualisation can also be used for dynamic images for moving systems, such as locks and movable bridges, which can be used for showing the functionalities of the design. Dynamic images can confirm that the design meets the intended functionalities and use of the system that is going to be built. Currently, 3D visualisation is used within the civil engineering sector in the Netherlands is by means of BIM. Within the civil engineering sector in the Netherlands companies deviate in the BIM maturity level, as well as the multiple subsidiaries within the contractor firm VolkerWessels. According to the BIM wedge that is developed by Bew and Richards (2008), level 1 of the BIM maturity is implemented. Therefore, the use of 3D visualisation is implemented in all integral projects within the infrastructure branch of VolkerWessels. The 3D models are used for clash detection, reinforcement in concrete, immediate vicinity and quantities. Conclusion According the results of the gaming simulation, visualisation does not have an effect on the project performances time, costs and quality. A possible explanation that no effect of visualisation on the project performances has been found is that the physical model of the hydrogen factory in the ontwikkelstraat was only used during one of the follow-up measurements. During the other two follow-up measurement, the physical model was not used by the participants. The follow-up measurement where the physical model was used, it was used by one participant, who built the model. However, the participant did not communicate his findings from the physical model to the other project team members. Therefore, there are not enough results to conclude that there are no effects of visualised validation on the project performances. From observations during the gaming simulation, it can be concluded that not the availability of the 3D model, but the standard practice of project teams is the issue. This research aimed to fill the theoretical gap on the effects of 3D visualisation on design validation. Although this aim is achieved, every research has to make compromises to stay within scope. The main limitations are presented in the itemisation below. • Lack of proper literature regarding the conditions for sufficient design validation; • Small sample size of the gaming simulation, which makes it harder to generalise the findings; • Time constraints during the gaming simulation, the participants are under a lot of time pressure during particularly the design phases. Therefore it is possible they overlooked the physical scale model. Soms suggestions for further research are: • It is found that not the availability of the 3D visualisation is the problem, but the standard practice of project teams play a role. Therefore, research on the problems of implementation of 3D models during design validation and how to solve these problems would b relevant for the whole construction industry; • During the project evaluations, it has been found that the way of handling changes in the contract after procurement is challenging. It turns out that validation of the design is often obstructed because the results of the validation process can result in uncertainties for all actors in a construction project. Research on the politics behind the changes in construction projects is relevant, because of the uncertainties these politics bring in the development of projects and the relationship between all actors that are involved; • Lastly, further research on the interaction between client and contractor is needed. Research on the development of achievable strategies to manage the counterbalance between collaboration and competition between client and contractor would be significant. Recommendations for practice Based on the literature and project evaluations, a proposed approach, called visualised validation approach (VVA) is developed. The VVA is a combination of two different project management methodologies, which are the V-model and the scrum. During the System design phase, three activities are essential: determining V&V management plan, requirement validation, and interaction with the client about the intended use of the system. The preliminary design phase is an iterative process, with scrum design sprints and validation sessions with the client and stakeholders. The biggest challenge in the construction industry is the implementation of the visualised validation approach. According to an internal investigation, the accessibility of the process and in particular the 3D mod- els have impact on the implementation of the VVA. Several employees indicated that they are reserved to use new approaches because of the lack of skills or manpower.

An increase in air traffic demand, aging pavement infrastructure, and limited funds make pavement management decision making a difficult process. Pavement management systems (PMS) are essential for pavement management. PAVER, the most common airport pavement management system (APMS) assists pavement managers by identifying the surface condition of pavement sections, however it’s based only on the pavement condition index (Greene, Shahin, & Alexander, 2004). Other criteria are taken into account by pavement managers to select the pavement sections that will be maintained or rehabilitated; however these criteria are not included in PAVER or any APMS. For pavement managers, it is very difficult to select the pavement sections that will be repaired because not all the sections in need of M&R can be selected. This research has focused on developing a pavement management decision making tool to help pavement managers prioritize and identify the sections that need M&R, based not only on PCI but on all relevant criteria needed for airport pavement management. The main research question to be answered in this research is: How can airport pavement management decision making be improved by means of data to identify the pavement sections that need to be maintained or rehabilitated? The chosen methodology to identify pavement sections in need of maintenance is the absolute Analytical Hierarchy Process (AHP). This research has explored the applicability of this methodology for prioritizing pavement sections in airports. Three major airports in Europe have been chosen as case studies for this research: Amsterdam Airport Schiphol, Brussels Airport, and Heathrow airport. Based on literature research and on these case studies, the required criteria used in airport pavement management for identifying pavement sections to be maintained have been identified. The required data for all criteria has also been identified. Based on all the criteria, the required data, and the AHP methodology, the mentioned tool has been designed. The design of the tool was presented to expert pavement managers to corroborate its applicability before its development. The tool has been developed in Microsoft Excel and its applicability has been illustrated by using partially real data and partially fictitious data. The AHP was successfully implemented allowing pavement managers prioritize large amounts of pavement sections, considering all relevant criteria. This research has revealed that pavement management decision making can be improved by means of data, by the development of tools like the one proposed in this research. It was also shown how big volumes of data can improve pavement management decision making, providing managers relevant information like the remaining structural life of pavement sections. Airports can benefit from this research by reducing the required time for prioritizing pavement sections and selecting those to be maintained or rehabilitated, by minimizing the amount of time spent on projects that will be disregarded, and by strengthening or facilitating the application of predictive maintenance.

It is desirable to predict construction cost with a high level of accuracy in the early phase to compare the budgetary with feasibility determinations. Additionally, it is required to be as quick as possible. However, the accuracy of the cost estimation depends on the design details which are extremely limited in such an early phase, rendering numerous uncertainties and dynamics which are hard to control and foretell.

Currently, diverse approaches and techniques are being used and refined to reach the ultimate goal; the cost estimation is to accurately forecast the final cost of a project with no design details available. Generally, estimators’ experience plays a critical role here, and the availability of historical cost data is also crucial. The process is significantly dependent on an export-driven approach. However, decisions made by experts can be subjective and error-prone especially when the relationships between cost drivers and the target cost are not fully understood or even identified. Consequently, cost estimation to a fair level of accuracy is hardly possible to achieve manually within a restricted time. In recent years, civil engineering domain has begun to consider machine learning technique as an optimal approach in tackling the predictive problem through a data-driven approach. Adaptive Network-based Fuzzy Inference System (ANFIS) (a hybrid model of Artificial Neural Network and Fuzzy Inference System) is advantageous in managing uncertainties and representing knowledge. This research aims at investigating the applicability of using the ANFIS for cost estimation during the conceptual phase.

Blockchain technology is able to guarantee an immutable data record which could avoid disputes and litigations. It could also significantly reduce time and costs through process automation. The technology presents a potential solution for the many legal barriers in BIM, regarding ownership of information. This thesis will continue research based on these hypotheses by experimenting within a project transcending partnership in the Dutch social housing sector. For these partnerships alignment of business objectives and commercial interests is still lacking. An overarching goal through benefit-sharing agreements could be implemented by blockchain-enabled contract management. This could potentially result in better financial control and a lucrative business case, which are instrumental for actual adoption of new innovations.