This thesis studies the integration of sustainability selection criteria into construction procurement. Using an Agent-Based Model (ABM), the study simulates single stage sealed tender processes also called first-price sealed bid auctions to analyze how different sustainability scoring rules influence bid pricing, contractor participation, and market competitiveness. The research examines the current state of the art of modelling in construction procurement and studies current practices for integrating environmental sustainability into public and private procurement processes.

The resulting model is grounded in utility theory and incorporates empirical data from construction management literature to model contractor decision-making under various sustainability-driven auction scenarios. The study evaluates the most common current sustainability scoring rules in practice—minimum requirements, flat discounts, and scaling discounts—and compares their effectiveness in promoting sustainability while maintaining market competitiveness.

Applying the model, the study simulates policy scenarios for implementing sustainability criteria and scoring rules over multiple tenders. These scenarios are designed to optimize for maximum sustainability investment from contractors while ensuring equitable access to sustainable construction opportunities. The findings suggest that an adaptive approach, combining minimum requirements and scaling discounts over time, is optimal for achieving sustainability goals in the construction sector.

The model is validated through pattern-oriented modeling (POM), and the findings are further scrutinized by expert interviews for alignment and use in real-world practices. This research serves as a proof of concept for applying auction theory, which has seen use in other sectors, to improve the impact of sustainability criteria in construction procurement. The study hopes to provides a step toward designing more effective procurement strategies that align market behavior with environmental goals.

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

The construction industry is transitioning from traditional methods to an industrialized approach, aligning more closely with manufacturing systems where operational efficiency is key to project success. This shift is notably challenging within the prefabricated construction supply chain due to the complexity inherent in combining construction and manufacturing systems, each with its unique characteristics that differ from simpler systems. To effectively manage these complexities, digital twin-enabled simulations offer valuable insights into system behavior, enabling more effective management strategies. However, implementing these advanced simulations requires sophisticated techniques, as current methods often focus on single aspects, neglecting other crucial elements of supply chain management. This paper addresses these challenges by proposing a novel digital simulation approach that enhances supply chain coordination through a mixed-method framework. This framework integrates both micro-level constraints and macro-level tactical decisions, creating feedback loops between different layers of the system. A case study is conducted using supply chain tactics involving inventory and capacity buffers based on the developed model. The study contributes to both theoretical knowledge and practical applications by providing a comprehensive framework for understanding the operational functions of digital twins and offering insights for future academic model development in the prefabricated construction supply chain. It enhances the understanding of operational efficiency and supply chain coordination in this evolving field for both researchers and practitioners.

The construction industry, known for its multifaceted operations and inherent complexities, has long sought ways to enhance efficiency, reduce waste and ensure timely project delivery. Risk management (RM) is crucial in preventing unexpected events and keeping projects on track, but current RM practices often fall short, leading to disruptions, inefficient resource use, safety issues, financial setbacks, and time overruns. These issues undermine the purpose of RM and jeopardize projects. Meanwhile, lean construction, grounded in lean thinking, offers a promising solution by identifying and minimizing constraints and inefficiencies. The goals of lean management, like waste reduction and value optimization, align well with RM, which focuses on identifying and mitigating threats to project success. Although there has been extensive research on lean management and RM as separate entities in construction and evidence of their integration in other sectors, there is a significant gap in the literature regarding their intersection in construction. This presents a research opportunity to explore how integrating lean management with RM strategies can improve project outcomes.
This research explores how lean tools can improve RM in the construction industry to enhance the ability of construction organizations to anticipate, mitigate, and respond to risks. It does so by analyzing the RM process to identify gaps and deficiencies in existing knowledge and practices. It then examines the nature and potential of lean tools to address these deficiencies.
The aim of this research is to understand the common issues in construction RM and highlight the potential for integrating lean tools into the RM process to achieve more resilient and efficient operations in the construction sector.
The result of the research is a conceptual lean RM framework, developed based on issues identified in the literature and supported by practical evidence, along with an analysis of lean construction tools and their potential to address chosen RM challenges. The framework spans the tender, planning, development, and construction phases. In addition to the framework, the approach to its application is outlined, including potential short-term and long-term benefits, inherent limitations, and potential integration challenges. The framework’s potential application environment is conceptually explored, and recommendations are offered for construction companies, standard developers, prospective users of the framework and future research efforts.

This thesis explores the external risks associated with the construction of tailings dams in the mining industry, with a focus on the recent regulatory changes in Mexico. These changes have introduced new complexities and uncertainties in project management, particularly during the tender and construction phases. The aim of this research is to address a gap in traditional risk management methodologies by developing a knowledge graph to identify and manage external risks more effectively, with the specific objective of “How to model an ontology of external risks in the construction of a tailings dam using empirical knowledge”. The scope of the study includes the analysis of external risks in the construction of a tailings dam, and the integration of ontologies to enhance contractors' decision-making processes during the tender stage.

The research employs a mixed-method approach, combining a literature review, case study analysis of the Mexican mining industry, and empirical data collection through interviews with industry experts. The methodology involves developing a knowledge graph using the Methontology approach, implemented through Protégé software. The results reveal various external risks, such as social conflicts, and activity-related and resource-related issues, that impact tailings dam projects. The knowledge graph created serves as a systematic tool for visualizing and analyzing these risks, with validation processes confirming its logical consistency and applicability.

The study concludes that external risks in the mining industry are multifaceted, encompassing social, financial, and resource-driven factors. The developed ontology provides a practical framework for contractors to identify and mitigate these risks more effectively, especially during the tender stage. The research recommends further development and application of ontology-based models in the industry to bridge the gap between theoretical risk management frameworks and practical real-world applications, thereby enhancing the overall risk management process in construction projects within the mining sector.

Empirical studies indicate an increasing inclination towards the adoption of integrated software systems that contribute to project management. To this moment, studies on the implementation of these integrated software systems tended to emphasise the benefit of such software. However, the analytical evidence to support such assumptions is relatively small. Therefore, the aim of this research is to investigate and analyse what is required to get the benefits when transitioning to an integrated software system to support project management in a construction consultancy company. As a result, the following research question has been formulated: ”What is required when making the transition to an integrated digital project management system in a construction consultancy company?”.

For the design of this research the framework developed by Purup and Petersen (2020) was used. The construction of this framework was based on the methodology of action research. The research was divided into three loops. Loop 1 identified the current knowledge regarding a fragmented or integrated system. Loop 2 investigated the use of the integrated system. Finally, in loop 3, all knowledge obtained from loop 1 and 2 was synthesised and developed into an answer to the research question. To answer the research question, a literature review and a case study for which semi-structured interviews were conducted among project managers, project leaders and experts on the implemented system.

A limitation of this study is that the interviews were conducted among users of the system with an age between 24 and 35 years. As a result, no statement can be made on the relationship between age and acceptance or motivation towards transitioning to a new integrated project management system. Therefore, the recommendation is to conduct the interviews in a follow-up study among users of the integrated system whose age distribution is more distributed.

The research revealed that transitioning to an integrated project management system affects the people, organisation, and process within a company. The users of the new system need to embrace the new system and learn to work with it. The transition phase requires a time investment before the benefits will be felt. In addition, an integrated system requires a standardised structure. With this standardisation, certain activities can be automated. This results in saving time. It can therefore be concluded that transitioning to an integrated project management system, after time has been invested for the transition phase, improves work efficiency and can save time.

This research can contribute to the organisations considering implementing an integrated digital project management system. Since more understanding has been generated on what to expect and what actions should be carried out to obtain the benefits of an integrated project management system.

This thesis explores the potential of utilizing Generative AI to enhance knowledge contextualization among stakeholders to promote circularity in the construction industry. Circularity, essential for sustainable development, demands a shift from the linear “take- make-dispose” model to a regenerative approach that minimizes resource consumption and waste. The construction industry, a significant contributor to global material use and waste, faces unique challenges in implementing circularity due to its technical complexity and the wide range of stakeholders involved. Consultants, who play a crucial role in bridging the gap between technical knowledge and practical application, are central to overcoming these challenges. This study focuses on consultants and their ability to disseminate and contextualize knowledge, a process hindered by technical language, the varying expertise of stakeholders, and the multi-disciplinary nature of construction projects.
The research investigates the current applications of Generative AI, with a particular focus on ChatGPT, as a tool for knowledge contextualization in circular construction projects. Through a comprehensive literature review and qualitative data collection from semi-structured interviews with industry professionals, this thesis identifies both the barriers to effective knowledge-sharing stakeholders encounter in circular projects and the potential affordances of ChatGPT in overcoming these challenges. The state-of-the-art capabilities of ChatGPT include features such as contextual comprehension, few-shot learning, and coherent content generation, which allow it to process and present complex information in a user-friendly format tailored to the specific needs of stakeholders. Its affordances, such as knowledge retrieval, organization, and tailoring, enable ChatGPT as a powerful tool to create customized knowledge outputs, making it particularly suited for addressing the diverse and technical demands of the construction industry. The analysis of the interviews highlights that the limited understanding of circularity principles among stakeholders stems from the complexity of technical documentation, the information overload of the documents regarding circularity, and the lack of tailored information that adapts to the specific needs of different actors and projects in the construction process.
The thesis investigates the possibility of ChatGPT as a solution to these issues by dynamically tailoring technical information to match the expertise and tasks of diverse stakeholders. The research studies how ChatGPT's features and affordances in knowledge management can enhance the contextualization of circularity knowledge, improving the accessibility and usability of complex information for non-experts. This approach fosters better collaboration among stakeholders and accelerates the adoption of circularity practices, often slowed by misunderstandings and technical jargon.
The findings suggest that while the potential of ChatGPT in knowledge management is vast, its implementation is still in the early stages. The study proposes a framework for integrating Generative AI into the knowledge-sharing processes of the circular construction industry, aiming to optimize knowledge dissemination and improve stakeholder comprehension. The framework is based on tailoring the capabilities of AI to support consultants in their pivotal role of contextualizing knowledge. By addressing the identified barriers to the adoption of this tool, this thesis contributes to the ongoing discourse on sustainable construction practices and highlights the importance of technology in advancing the industry towards circularity.
This thesis concludes by offering practical guidelines for the integration of Generative AI in circular construction projects, underscoring the need for further research into its long-term implications. The proposed guidelines for integrating ChatGPT into the construction industry focus on three key phases: preparation, where stakeholders are trained to understand the AI’s capabilities; pilot testing, where AI-assisted knowledge contextualization is trialed in a controlled setting; and deployment, where the AI is fully integrated into project workflows with ongoing monitoring to ensure its effectiveness in enhancing communication and knowledge sharing. The research lays the groundwork for future studies aimed at refining AI applications in this context and ensuring that the construction industry can meet its sustainability goals through improved knowledge management practices.