This thesis addresses the practical implementation of component-level Material Passports (MPs) in the construction sector to support circularity. While MPs offer potential for material reuse and lifecycle transparency, they face persistent barriers, including lack of standardization (Kedir, 2021) and the need for coordinated stakeholder involvement (Honic et al., 2019).
The objective of this research is to develop and evaluate a structured MP workflow applicable to new buildings from early design to manufacturing phases (RIBA Stages 1–5). A Design Science Research method was used across two iterations. In the first, literature reviews and expert interviews identified workflow objectives and stakeholder responsibilities, resulting in an initial workflow. The second iteration involved improving the workflow and testing it through a template-based data collection exercise within the Stanford AEC Global Teamwork course, where feedback was gathered from multidisciplinary participants.

Best practices observed include workflow clarity, ease of use, alignment with existing carbon accounting practices, and stakeholder agreement on responsibilities. Key challenges include difficulty in disassembly data interpretation, time-consuming manual data extraction, and hesitance from subcontractors accustomed to conventional practices. The workflow currently lacks automation and has limited real-world validation.

The scope focuses on component-level MPs, omits product-level inputs, and is limited to academic testing within a European policy framework. The full lifecycle impact of MPs remains theoretical, and applicability may be limited in regions without supportive reuse markets or regulatory incentives. Despite these limitations, the proposed workflow lays a foundation for future real-world MP adoption and suggests areas for expansion, including AI-assisted data extraction and integration across all lifecycle stages. 

Subcontractor evaluations are a critical component of post-project reviews in the construction sector, significantly influencing future procurement decisions, subcontractor selection, relationship management, and risk mitigation strategies. Despite their strategic value, existing subcontractor evaluation practices often suffer from several persistent shortcomings: high subjectivity, lack of standardized performance metrics, limited documentation, exclusion of subcontractor perspectives, and minimal use of structured or historical evaluation data. In response to these challenges, this thesis examines whether and how integrating ChatGPT 4.0 with a Multi-Criteria Decision Analysis (MCDA) framework can enhance the consistency, objectivity, and strategic value of subcontractor evaluations within HOCHTIEF Nederland.
Building on a comprehensive literature review and exploratory interviews with key procurement and project staff, the study identifies gaps in traditional evaluation processes, particularly the overreliance on static rating templates and the absence of dual-perspective analysis. To address this, a novel AI-enhanced evaluation framework is developed using a design science methodology. The framework enables structured performance assessments based on both internal HOCHTIEF feedback and subcontractor self-evaluations. ChatGPT 4.0, embedded in HOCHTIEF’s internal AI assistant “NextChat” supports this process by interpreting qualitative data, prompting for clarification when needed, generating justifications for ratings, and summarizing insights into actionable reports.
The framework was implemented in a pilot case study involving a subcontractor working on a real HOCHTIEF data centre project. Evaluation inputs from both parties were processed using the AI assistant and benchmarked against HOCHTIEF’s existing manual evaluation methods.
Validation of the framework was multi-faceted. Process validation demonstrated that AI-generated reports aligned closely with manual evaluations, with a Mean Absolute Percentage Deviation (MAPD) of less than 10%, indicating high accuracy. Stakeholder validation was conducted through structured surveys with HOCHTIEF personnel, assessing insightfulness, transparency, clarity of follow-up queries, added value, and perceived limitations. The results were consistently positive.
While the results indicate strong potential for improving subcontractor evaluations through AI integration, the study also highlights critical limitations and risks. These include the need for high-quality, context-rich input data, the need for human oversight and verification of the results, data availability challenges, and the necessity of embedding the tool within existing procurement databases to ensure organizational consistency. Furthermore, successful implementation depends on training and change management strategies, particularly in organizations with limited digital procurement maturity.
The study contributes theoretically by empirically validating the integration of AI and MCDA in construction procurement and practically by providing a scalable tool that enhances the structure, transparency, and usability of post-project subcontractor evaluations. It is supported by established adoption frameworks, including the Technology–Organization–Environment (TOE) model and the Unified Theory of Acceptance and Use of Technology (UTAUT). Overall, this research offers a data-informed, dual-perspective, and AI-supported approach to subcontractor assessment, positioning it as a robust enhancement to current construction management practices.

The energy transition requires rapid scaling of renewable energy systems, with offshore wind playing a central role in Europe’s climate and energy security ambitions. The Netherlands, through TenneT’s 2GW Program, is pioneering a standardized 2 GW high-voltage direct current (HVDC) grid connection system. This thesis investigates how SE can address challenges in offshore grid projects and maximize benefits for Transmission System Operators (TSOs). Using a triangulated methodology: literature review, document analysis, and semi-structured interviews , the study evaluates SE implementation in the 2GW Program. Findings highlight four key themes: (1) requirements management, where structured and lifecycle-oriented specifications enhanced clarity, traceability, and collaboration; (2) verification and validation (V&V), which reduced risks and improved accountability through systematic compliance; (3) digitalization, enabling transparent, traceable, and program-wide data integration; and (4) external influences, such as contractual frameworks, supply chain disruptions, and organizational culture, which shaped SE effectiveness. While SE demonstrated significant potential, its benefits remain partly unrealized due to cultural inertia, contractor maturity gaps, and time pressures. The study recommends phased SE adoption, front-loaded requirements engineering, client-side V&V ownership, engineered digital tools, and embedding cultural change management. For researchers, it suggests broadening perspectives beyond the client to include contractors and assessing SE processes in later program stages. This research contributes to advancing SE as a governance and delivery mechanism for large-scale offshore infrastructure, supporting TSOs in delivering renewable grid connections efficiently.

This thesis addresses a central challenge in infrastructure project management: despite sophisticated monitoring tools like Earned Value Management (EVM), teams struggle to proactively identify and address root causes of performance deviations. The research investigates why EVM-based systems often fail to deliver actionable insights and explores whether integrating Root Cause Analysis (RCA) with Bayesian-enhanced EVM can bridge this gap. Anchored in a large infrastructure project at Schiphol Airport, the study combines literature review, interviews, Bayesian modelling in GeNIe, and validation with industry stakeholders. The findings present a dual-layer framework: (1) a Bayesian Network linking performance indicators (SPI, CPI) to risk drivers, enabling backward reasoning from deviations to causes, and (2) integration of RCA to reveal systemic factors behind disruptions. Validation confirmed the tool’s conceptual usefulness but highlighted adoption challenges, including dashboard integration, model complexity, and cultural readiness. The research concludes that embedding RCA into Bayesian-enhanced EVM shifts it from a reactive tool to a forward-looking enabler of organizational learning. However, technical robustness must be complemented by openness, accountability, and a culture of learning. Recommendations include piloting in mid-complexity projects, calibration with real data, and building scenario libraries from historical RCA cases. Overall, the work reframes risk management as a dynamic, learning-oriented process, emphasizing that better project outcomes rely not only on better data, but also on diagnosis, culture, and collaborative decision-making

Cities like Rotterdam, facing increasing flood risks, require integrated planning tools that account for the interplay between flooding and urban mobility. Despite advances in hydrodynamic and agent-based transport modelling, practical frameworks for coupling these models remain underdeveloped. This thesis develops an XLRM (eXogenous uncertainties, Levers, Relationships, Metrics) framework to guide the integration of flood simulation models (e.g., 3Di) with transport modelling (e.g., MATSim/SUMO) for robust decision making.
Drawing on literature review and expert interviews, the research identifies key sources of uncertainty and policy levers relevant to Rotterdam’s flood–transport system. The main contribution is the formalisation of the Relationships (R) layer: a set of rule-based protocols translating flood-model outputs into dynamic adjustments in transport models, based on empirically calibrated depth–speed functions and iterative data exchange at operational time steps. Optional feedback loops allow planners to test intervention timing and road management strategies under uncertainty.
Validation with domain specialists confirmed the framework’s technical soundness, logical clarity, and practical transferability. The resulting XLRM register offers planners a blueprint for embedding flood–transport interactions within digital-twin environments, enabling stress testing of adaptation strategies and supporting resilient, evidence-based infrastructure planning under climate uncertainty.

Digital technologies have increasingly shaped the global construction industry with the fourth industrial revolution (Industry 4.0) emerging as a widely discussed concept. In this context, Construction 4.0 represents the sector’s response to Industry 4.0 principles, particularly through innovative technologies such as Building Information Modeling (BIM). Accurately defining and verifying requirements is essential for project quality and safety, but complex projects often involve thousands of requirements that must be manually checked. As projects grow more complex, there is a clear need for automated verification. This research explores the process of semi-automated requirement verification. The main research question is: “How can a (semi-)automated process be developed to validate and verify requirements within a 3D model?”. This is supported by four sub-questions exploring current limitations, existing tools and methods, and key development stages of automated requirement verification.

A literature review highlights that project requirements are typically derived from the client’s vision and form the basis of contractual obligations. Successfully extracting these requirements is important for defining the project scope and achieving client satisfaction. The literature study also explores various international and national BIM standards, which are adopted to different extents across countries worldwide. Several verification tools were evaluated such as Solibri, Navisworks, BIM Assure, SMARTreview and Verifi3D. From the comparison, Solibri and Navisworks emerged as the tools that provide the most comprehensive functionalities. In terms of requirement structuring methods, the RASE language was identified as the most promising approach due to its clarity and expressiveness.

The research used the Oosterweel project as a case study that is being developed in Antwerp (Belgium) with the aim of completing the city’s highway ring road. The verification process of requirements is broken down in five key steps. The first step focused on the classification of
requirements into geometric and non-geometric requirements using a machine learning model in Python. The second step involved manual rule interpretation using the RASE mark-up language. The third step focused on preparing the model data by filtering the model and performing semantic enrichment. Step four looked at the actual execution of the verifications where different types of requirements were distinguished in classes based on their computational complexity. The last step focused on reporting of the verification results in a Common Data Environment.

Implementation challenges during this process included ambiguous requirement texts, inconsistencies in model data and a fragmented workflow. The insights from the literature review, findings and interviews were combined into guidelines which are divided into three levels: strategic, operational and BIM level. The strategic level focuses on guidelines from the organizational point of view. The operational level looks at how strategic intentions can be translated into practical actions. The BIM level addresses guidelines that relate to the practical
use of BIM. The guidelines are validated through interviews with professionals.

The results of this research show that a semi-automated approach to requirement verification within the BIM environment is feasible and significantly more efficient than current manual methods. Research limitations include the limited scope of one case study and the internal
validation of the guidelines. Based on the research, multiple recommendations were given for future research and practice. Practical recommendations include using a structured approach and the use of open standards. Recommendations for future research include the further
investigation of AI beyond the current machine learning model for requirement verification and the investigation of the impact of the automated verification process.

This thesis explores how generative AI (GenAI) can assist in reviewing open infrastructure tenders, focusing on consultants working with contractors. It examines how individuals interact with GenAI, what motivates them to adopt or avoid it, and how these interactions influence the tender process. At the centre is the concept of deliberation, a reflective exchange between humans and GenAI aimed at deepening understanding rather than reaching agreement. The research asks how people experience this process within project-based organisations.
Using a qualitative approach, the study combined a literature review with an experiment involving a realistic tender case. Individuals and GenAI reviewed a tender section, guided by a structured framework. They later discussed their experiences in groups, and industry experts provided further insights.
The Activity Theory was applied, offering a lens to explore how GenAI operates within broader socio-technical systems. Central to this approach is the concept of contradictions, which are understood as tensions within or between components of an activity system that may either act as drivers or barriers for the adoption and use of GenAI. The Human–GenAI Deliberation framework provided structure for meaningful engagement between users and GenAI.
Younger and less experienced participants seemed more open to revising their views, while older users were generally more fixed. Active engagement, including questioning the GenAI, led to more shifts in opinion and was tied to higher trust. However, GenAI’s servile behaviour, rarely doubting the input of the individuals, reduced its impact. Key drivers for use included structured guidance, the objectivity of GenAI, and curiosity reinforced by peers. Barriers involved scepticism, poor prompt formulation, and limited awareness of GenAI’s potential.
The study highlights the importance of building trust and supporting users with clear guidance and training. A major barrier is the lack of knowledge about how to interact effectively with GenAI. Providing users with practical support can build confidence.
Organisations should invest in targeted training, promote informal peer learning, and position GenAI as a collaborative partner. Introducing a GenAI-supported review phase near final tender submission may support deeper reflection. Future research could further validate these findings, explore better prompting strategies, and examine broader applications beyond consultancy.

The design phase of Dutch infrastructure projects is often delayed due to fragmented data systems and a lack of real-time insight. Many companies use multiple, non-integrated platforms, making it difficult to maintain a clear overview of design progress, responsibilities, and deadlines. This research addresses the problem by developing the Design Phase Monitoring Tool (DPMT)—a practical, data-driven solution based on the Design Science Research Methodology (DSRM).
The DPMT goes beyond a standard dashboard. It offers a structured approach to monitoring design activities, using clearly defined indicators and organized data flows to create a comprehensive overview. Developed in collaboration with Heijmans, the tool brings together dispersed information—such as document status, milestone alignment, and verification progress—into a single visual environment. This improves situational awareness, allowing project teams to detect issues earlier and act more proactively.
Validation through a focus group confirmed the tool’s value: users appreciated the improved visibility, the ability to make better-informed decisions, and the shift from reactive to proactive management. Experts suggested the DPMT should become a standard part of the design process. However, the prototype currently relies on manual data entry and static planning dates. Automating these elements and adding predictive features could significantly enhance its impact.
In conclusion, this research shows that a structured, digital approach to design monitoring is both feasible and beneficial. The DPMT helps project teams gain control, improve coordination, and reduce delays. It also lays the foundation for future innovations, such as real-time integration and predictive analytics, and raises the standard for design phase performance in infrastructure projects

The construction industry relies on multidisciplinary teams, each working within its configuration framework to achieve shared project goals. As projects grow increasingly complex, the scale and interconnectivity of configurations expand, leading to a higher likelihood of clashes between components. This complexity makes effective Configuration Management (CM) increasingly challenging. Over the past two decades, Building Information Modeling (BIM) has emerged as a promising solution for managing complex construction projects' configurations. However, systematic integration of CM within BIM remains insufficient. To address this gap, this research aims to explore how Model-Based Systems Engineering (MBSE) can enhance configuration processes within a BIM environment. The primary objective of the study is to develop and demonstrate a structured approach that leverages MBSE to visualize and manage interdependencies between building components across different disciplines, thereby improving the integration of CM in BIM workflows. The main research question driving this study is: How can engineers in a BIM environment leverage MBSE to visualize and manage interdependencies between building components across different disciplines? To support this research, the study addresses the following sub-questions: 1. What MBSE methods can be adapted to manage design changes in a BIM environment? 2. How can a mock-up prototype be developed to illustrate the practical implementation of MBSE in BIM? 3. What are the potential applications of MBSE for estimating the impact of design changes in real-world construction projects? Key findings highlight MBSE’s transformative potential to improve decision-making in complex construction projects. By integrating BIM with MBSE, engineers can visualize and analyze interdependencies, enabling early conflict detection, enhancing multidisciplinary collaboration, and streamlining the change management process. Real-time updates and automated simulations further support effective impact analysis. The Design Structure Matrix (DSM) offers a systematic framework to anticipate the cascading effects of design changes across subsystems while improving cost and risk estimations. Despite its advantages, MBSE implementation faces significant challenges, including technical barriers, tool integration issues, scalability, accessibility, and organizational resistance to change. This research offers actionable recommendations to address these challenges, such as standardizing processes, adopting incremental implementation strategies, promoting cross-disciplinary training, and enhancing BIM tools with MBSE features. Ultimately, this research demonstrates how MBSE, when integrated with BIM, can transform CM practices by improving efficiency, reducing errors, and enhancing collaboration between different disciplines during design changes.

The Architecture, Engineering and Construction (AEC) industry, traditionally characterized by
hierarchical structures, is undergoing a transformation driven by technological advancements.
Despite these innovations, many global companies in the industry have retained their
conventional organizational and governance frameworks. This study investigates the potential of Decentralized Autonomous Organizations (DAOs) to introduce a bottom-up management approach in the ACE industry, focusing on their transformative impact on architectural design methodologies.
The research addresses a significant gap in the literature regarding the management and
governance frameworks necessary for effective collaboration and distributed architectural
design within DAOs. The study employs an extended single case study methodology, utilizing
semi-structured interviews, ethnographic observations on Discord and Telegram, and a
comprehensive literature review. The case study centers on ArchiDAO, the first architect-founded DAO, and its participation in the Evolo Skyscraper Design Competition 2024.
ArchiDAO's innovative vision and task-based model are examined to understand how DAOs
can manage collective and distributed architectural projects. Key findings reveal that DAOs
can enhance project management through transparent and decentralized decision-making
processes, equitable reward systems, and advanced communication tools. However,
challenges such as role ambiguity, inconsistent task completion, and limited blockchain
utilization were also identified.
This study's outcomes highlight the effectiveness of DAOs in promoting decentralized
leadership and collaboration, reflecting practices observed in young architectural collectives.
These collectives, often formed in response to traditional top-down approaches, emphasize
multidisciplinary and non-hierarchical structures that align with the principles of DAOs. The
research contributes to the adaptability of architecture firms in the evolving AEC industry by
providing insights into the integration of decentralized models for more effective and
inclusive project management.