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.

The current research stems from the motivation of controlling the phenomenon of traffic congestion in urban areas that can be deteriorated due to the implementation of a construction project. Nowadays, it is frequently observed that construction activities in complex urban environments appear undesirable effects on the communities surrounding the location of the construction project having as a focal point the “construction traffic”. Nonetheless, existing studies indicate traffic congestion is a significant contributor to the inefficient management of construction logistics. Unsuccessfully managing a project’s construction logistics is strongly correlated with the unsatisfactory performance of the construction process mostly due to the occurrence of delays in the implementation of the construction activities related to incorrect delivery of building materials and construction equipment. Likewise, inefficient management of construction logistics can lead to unmanageable traffic levels derived from additional transportation volumes related to the construction process. Since the multi-faced issue of traffic congestion can cause both a negative “domino effect” on the construction activities towards the realization of a construction project as well as unfavorable effects on urban city’s living conditions, positively impactful ways ought to be researched for reducing the adverse consequences of uncontrollable traffic. This thesis works for designing and developing a transportation prognosis model that can predict the extra transportation volumes on the location of the construction project due to the transportation activity of the needed vehicles along the construction process, offering an insight into traffic conditions of the examined relevant road. In this way, the designed prognosis model can be used as a proactive tool since it indicates if and when uncontrollable traffic levels can occur.

FRP is increasingly utilised in the built environment, following successful implementations in the aerospace and marine industry. However, it exhibits poor stiffness and stability compared to conventional materials such as steel, making it challenging to satisfy serviceability and comfort requirements. Optimising FRP to address these challenges will allow for lightweight solutions with long service lives which could contribute to a cost-efficient reduction of carbon footprint. To this end, an optimisation tool for the preliminary design of bridges using FRP is presented in this thesis report.

The research explores the feasibility of adopting FRP in the main load-carrying system of pedestrian bridges and develops a framework for the concurrent geometry and material architectural optimisation of said structures. The study aims to achieve significant cost- and carbon footprint reductions in
monocoque FRP bridges by employing a numerical optimisation approach.

The optimisation tool utilizes the computer-aided geometric design (CAGD) software Rhino® and its parametric interface, Grasshopper®, to concurrently optimise the shape and material architecture of the bridges. Through the use of genetic algorithms, the framework overcomes FRP’s poor stiffness and
stability, and maximizes its unique advantages, including lightweight and high-strength properties, enabling free-form designs. This feat is achieved by implementing hybrid sandwich panels, comprising glass fibre-reinforced polymer (GFRP) and carbon fibre-reinforced polymer (CFRP) face sheets.
Satisfactory stiffness is ensured by defining deflection constraints, whereas constraints on the fundamental frequency and critical buckling load factor ensure adequate stability.

The research demonstrates promising results, showing potential cost reductions of up to 17% and carbon footprint reductions of up to 27.4% compared to a real case design carried out by FiReCo. However, certain limitations and areas for improvement are acknowledged, including the required run-time and the complexity of the solution space. Suggestions for enhancing the framework’s efficiency are proposed, including implementing orthotropic failure criteria and reducing the solution space through adjustments to ply thicknesses and foam core configurations.

Overall, the developed optimisation tool provides valuable insights and serves as a valuable resource for researchers and practitioners seeking sustainable and economically viable bridge designs. By embracing innovative solutions and eco-friendly materials, this study contributes to global efforts towards carbon neutrality and sustainable infrastructure development in the built environment.

Precast concrete appears to be promising in dealing with delays in housing construction, yet long lead times are apparent in this industry. Lead time refers to the time that elapses between placing and receiving an order. The construction company Janssen de Jong recognizes the problem of sub-components’ long lead times in precast concrete house-building. Sub-components are those products being integrated into the precast concrete components and they are supplied to the precasting concrete factory by various suppliers. The company seeks to optimize sub-components’ lead times, so as to increase its competitiveness. Accordingly, the current research focuses on identifying the sources of sub-components’ long lead times in precast house-building industry and optimizing them, in order to improve projects’ performance in terms of time and costs. The research draws on literature review, interviews, mathematical optimization modelling and case study.

Literature review and interviews with Janssen de Jong, a precasting concrete factory, a windows and frames supplier and an installation technology supplier revealed the large batch size of batch-and-queue manufacturing method, as the main quantifiable source of long manufacturing time, affecting significantly the delivery of sub-components and so, extending their lead time. Batch-and-queue utilizes the equipment to its maximum capacity, yet leads to stock creation at each production step. On the contrary, one-piece-flow manufacturing method (batch size of one), was indicated as a value-adding process worth to approach, yet not easily achievable, not so efficient and more expensive. One-piece-flow refers to moving the product step-by-step through each process step without non-value-added time. The conflicting interests of the suppliers and Janssen de Jong call for solutions that could satisfy both time- and cost-related interests.

The mathematical optimization model was built based on the identified conflicting relation between reducing costs and shortening lead times, in relation to the batch size. This was done considering the arguments in favor of and against both manufacturing methods. In this way, the bias is reduced. Weights are assigned to each of the objectives, denoting their relative importance to the client. A set of optimal lead times and costs is obtained, after running the model for each supplier separately, enabling Janssen de Jong to improve the project performance as per the priorities each time.

A project of 23 precast concrete houses was utilized as a case study for the model validation. Out of the involved suppliers, only the windows and frames supplier made some data accessible. This supplier already follows one-piece-flow. Access to financial data was restricted, so, a supplementary Greek company, which is active in the same field and it is highly representative of the case, was utilized. The developed model yields reasonable results compared to the real figures.

Concluding, the model proves to be effective in optimizing sub-components’ lead time in precast concrete house-building industry. However, it is recommended the model to be validated using companies from other fields (e.g. installation technology company) and incorporate further information in a future version –complex to include this in the current thesis– to achieve even higher levels of realism and applicability.

Flood risk models are frequently used to analyse the climate- and socio-economic-driven impact of flooding hazards. However, model validation is rarely done adequately due to the rare occurrence of floods and even less frequent reporting of corresponding damages.

In this research, validation is defined as the process of ensuring that a model performs within a range of accuracy and precision, satisfactory for its intended use. To guide experts in their validation efforts, a four-phased framework is developed to validate flood-event damage estimations, created with hazard x exposure x vulnerability models.

The framework was applied to two damage estimates created by the Global Flood Risk Tool (GFRT). 1) For damage caused by the Limburg 2021 river flood (The Netherlands - Europe) and 2), for damage caused by a 2019 hurricane-induced coastal flood in Beira (Mozambique - Africa). For the Limburg case, total direct damage was determined at 349,4 million euro. An initial model overestimation of 34% was caused primarily by a large exaggeration of exposed agricultural surface area, and significant modelling errors of linear infrastructure. Furthermore, an uncertainty range was quantified between 271,8 (-23%) and 388,2 million euro (+11%) due to uncertainty in residential assets (across all three model parameters) and an uncertain exposure parameter of agricultural assets.

To create additional damage estimates for verification, a Structured Expert Judgement (SEJ) experiment was executed with ten flood-damage experts. Due to the high experiment cost and low expert-informativeness, the method is currently not advised as a validation approach. In situations with limited data, experts may still be a relevant information source.

For Beira, damage was determined at 8,1 million US dollar. The model underestimated damage by 82% due to errors in infrastructure, industrial, and commercial assets. Besides, overestimations were found for informal residential- and agricultural assets. The estimate ranges between 5,2 (-36%) and 13,2 million US dollar (+62%). This range excludes uncertainties at port and industrial assets, as insufficient information was available. Contrary to the Limburg case study, insights from the plausibility assessment were too uncertain for quantification, thus the validated estimate is based on damage- and construction cost data. Novel techniques were used to disaggregate the compound damage data, such as comparing wind and flood vulnerability curves and applying employee-based estimations.


The significantly altered damage estimate for both case studies demonstrates the usefulness of the framework. However two main limitations remain: first, lacking information on direct damage to critical infrastructure hinders validation.
Second, additional detail in data is required to allow parameter calibration that increases accuracy across multiple flooding scenarios. Therefore, the main recommendation for future research is to increase the detail in damage data reporting so that parameter calibration is supported. This may be done by increasing the spatial resolution of reported damages or adding additional variables such as inundation depth in reports.

Inaccurate truck cycle time (TCT) prediction in earthworks impacts construction projects because more equipment and human resources must be added to complete the project. It also increases fuel consumption and emissions from the machinery. However, the current method gives inaccurate prediction because of subjectivity and human error.
This research aims to utilize the historical data for improving the accuracy of TCT prediction in earthworks. Two historical data are explored, such as manual and automated data provided by BAM, using a machine learning approach in which regression techniques: Multi Linear Regression (MLR), Support Vector Regression (SVR), and Artificial Neural Network (ANN).
The result concluded that automated data could develop predictive models because of its quality and variance. ANN develops most predictive models with feature combination one or two, where is distance is the important feature. The benefits of the predictive model are analyzed by comparing them with the traditional method in predicting truck cycle time. The models are more accurate in predicting truck productivity and have lower inefficient truck cycle time than the traditional method. The reduction of inefficient truck cycle time can reduce the cost for fuel and drivers, fuel emission. Models also have intangible benefits to stakeholders, such as gaining partners trust and a better strategic plan to complete the project.
In conclusion, the historical data can improve the prediction accuracy of TCT and give benefits to stakeholders. And practitioners are suggested to raise awareness about the importance of data and improve earthmoving documentation for better predictive models.

Movable bridge decks experience critical expansion in summer, leading to uncertainty and unpredictability in its availability doe to improper docking and safety hazard. If the bridges are not cooled soon, the inertia of expansion stays, causing prolongation of availability problems. Structural health monitoring of such bridges with a predictive maintenance approach can help plan remedial measures on the exact day and time. For the efficient design of such a structural health monitoring system, a combination of sensor system data and weather API data is tested. A machine learning approach of Gaussian process regression which can give the results on the prediction of critical expansion of bridge deck has been evaluated in this research project. Finally, a check on the transferability of the prediction model is conducted by application on another bridge data and its performance is discussed. Scenario analysis with savings in cost per scenario is also conducted with varying levels of potential unavailability penalty costs, which could be levied on an asset manager of a bridge if the prediction models of sensor system data set or/and weather API data set gives incorrect estimation. Such an analysis is done to justify the use of the prediction models in assumed scenarios to predict expansion of movable bridge deck