The increasing frequency of climate-related disasters poses significant threats to coastal communities, disrupting not only physical infrastructure but also the social, economic, and environmental systems that underpin daily life. Community resilience, defined as the ability to withstand, adapt, and recover from these disruptions, is crucial for ensuring the long-term sustainability of these areas. However, existing resilience frameworks often fall short as they lack cross-sectoral integration, do not account for the interconnectedness of infrastructure systems, and fail to adequately incorporate climate risks or adapt to local cultural and environmental contexts. Moreover, these frameworks frequently overlook the diverse perspectives within communities.

To address these gaps, the NERMI CCR (Coastal Community Resilience) Framework combines quantitative methods such as Principal Component Analysis (PCA) with expert consultations to refine 134 key resilience indicators across five dimensions: Social, Infrastructure, Environmental, Organizational, and Economic. This approach provides decision-makers with 110 fixed indicators and 24 context-specific indicators, ensuring flexibility and promoting an integrated, system-of-systems view of resilience planning across diverse coastal settings.

This research examines the resilience of the water sector after a societal collapse, defined by a significant loss of societal complexity. Through Exploratory Modeling and Analysis, this research evaluates how interconnected infrastructures respond to cascading failures caused by disruptions resulting from a societal collapse. The investigation centers on the interdependencies within the water sector, crucial for its function to abstract, treat, store, and distribute water. The model makes use of disruption curves affecting a network of interdependent components to simulate cascading failures, factoring in variables such as population change and contamination rates.

The research makes use of expert interviews and economic Input-Output tables to devise the dependency matrix, finding that accurate representation of dependencies is critical for meaningful results. The model allows for adaptation strategies, specific to the dependencies of a sector, which demonstrates potential improvements in water sector performance and suggests that adaptive methods may be more effective and less costly than redundancies.

While the model simplifies complex interactions and cannot fully encapsulate the breadth of societal collapse effects, it sheds light on the importance of dynamic dependencies and offers a framework for future studies. These could include integrating more intricate domain-specific models to refine the responses of sectors to disruptions and reduced input. The findings highlight the significance of adaptation as a means to enhance the water sector's resilience.

Earthquakes are among the most devastating natural disasters, causing widespread damage, particularly in urban areas. A critical but often overlooked component of post-earthquake emergency response is the role of debris removal in the initial response phase. While debris removal is typically treated as part of long-term recovery, its immediate integration could enhance access to casualties, enabling faster medical care. This study investigates the impact of different debris removal strategies on the effectiveness of emergency response in urban settings using an Agent-Based Model (ABM) developed on the NetLogo platform. The model simulates a virtual city, "Quakecity," with realistic urban features, including road networks, hospitals, building damage, and injured resident distribution.

Two primary debris removal strategies were tested: one based on population density and the other on proximity to hospitals. Additionally, the potential use of army vehicles for casualty transportation was examined. These strategies were evaluated under two earthquake scenarios, varying levels of resource availability for both ambulances and debris removal equipment, measured by assisted residents and unreachable residents.

Results demonstrate that ambulance capacity has a more significant impact on the number of assisted residents than debris removal alone, although both strategies improve response effectiveness, particularly in high-damage scenarios. The hospital-proximity strategy was most effective when resources were plenty, while differences between strategies diminished in resource-constrained conditions. The introduction of army vehicles as supplementary casualty transporters proved highly effective. This research highlights the importance of adapting emergency response strategies based on available resources and the potential benefits of incorporating debris removal into the immediate response phase.

The construction sector owns a bad reputation in terms of performing on time and within budget. The inherent characteristics of large construction projects of complexity and uncertainty play an essential role in such deviations. While current project management and risk management approaches cannot fully predict, prevent, and enable the successful conquering of disruptions, the resilience-based project management approach seems like a promising solution. However, the resilience concept is still new in the construction industry and few literature works tackled this area with low consensus. Whereas in practice, the concept is still not explicitly used. It is not clear yet what constitutes a resilient construction project and what elements contribute towards resilience. Therefore, this research aims to understand the resilience concept at the level of a construction project, uncover the elements contributing to building resilience and introduce these in one solid framework that aids in building, evaluating and enhancing resilience in construction projects.

Based on an inductive approach this research resulted in a framework, the PRL resilience framework for construction projects, based on theory and practice. To build the framework, related literature work was reviewed, and 16 interviews with professionals experienced in complex construction projects (infrastructure projects) were interviewed. For evaluation, the framework for three study cases and was found acceptable to satisfactory. The framework consists of 87 elements assigned into three dimensions (proactiveness, reactive capacities, and learning), and 15 related project management areas: Client management, stakeholder management, monitoring and control, partners, mother organization, risk management, project manager, project team, schedule management, change management, contract management, project management approach, information management, tender management and design management. The resilience of a construction project can be defined then as the ability of a construction project to overcome disruptive events (preserve its well-functioning and ability to perform to achieve expected targets) fast and without bypassing the current most valuable project objectives thresholds, enabled by proactiveness (Awareness, anticipation, alertness), reactive capacities (absorptive, adaptive, recovery), and learning.

Main future research work related to the resilience of construction projects is suggested as follows: (1) further research towards identifying the weights of each resilience element, (2) link project complexity elements (suggested using TOE framework by Bosch-Rekveldt et al. (2011)), to be used to design and tailor resilience elements from the PRL framework based on project-specific complexity scan, (3) Applying the PRL framework to different types of projects, (4) Investigating the concept of resilience on portfolio level and program levels.

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

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.