Cities are rapidly transforming, and infrastructures are not always resistant to these future developments. On a global scale cities are growing into mega cities, whilst counter urbanization causes significant population declines. To adapt infrastructures against these future developments, infrastructure asset management can be applied.

Infrastructure asset managers require reliable insight in future developments, to make their intervention decisions strategically. This involves assessing multiple variables, and their underlying relations. Infrastructure systems can perform multiple functions, have various connecting interfaces, and are mostly situated in a dynamically changing environment. Therefore, intervention decisions are subjected to dynamic complexity, and uncertainty. However, asset managers predominantly make large intervention decisions based on decision support methods of a static nature, which provide insufficient insight in the dynamic complexity, and uncertainty, of future developments. Since infrastructures are the backbones of local economies, a decision support method able to incorporate all relevant complexity, and uncertainties, is inadmissible for infrastructure asset management.

This study aims at improving large intervention decisions, by assessing dynamic complexity, and uncertainty, with multivariate simulation approaches as the exploratory system dynamics modelling and analysis approach (ESDMA), and adapting strategies for large intervention decisions with the adaptation pathways approach. The approaches were applied to a case study, which is a highly schematized representation of the city of Amsterdam, with its interconnected infrastructure network.

The study showed that the proposed approaches can improve large interventions decisions on an infrastructure network level, by adapting them dynamically over time to uncertainty, and complexity. The key findings include the identification of opportunities, ‘no regret actions’, dependencies, effects on interconnected infrastructure systems, and the required intervention timing for multi-dimensional functions. A most adaptive set of interventions for all future scenarios could be identified on the basis of costs, target effects, and possible side effects.

The construction industry is a large generator of waste. The interest in improving the resource efficiency is growing. A move towards a Circular Economy (CE) has been assigned as the solution for a more efficient use of resources in the construction industry. The aim of a circular economy is to create a system in which waste and energy leakage is minimised and resource input is avoided by closing the material and energy loops. Despite the advantages of CE, the construction industry has not yet made a significant step towards
the CE. The theory of Reverse Logistics (RL) could be the enabler of circular economy in the construction industry. RL in the construction sector is defined as the management of collecting, sorting, processing and reusing construction waste. In the field of RL, quantitative models have proven to be a successful tool to combine environmental goals with financial constraints. However, such models are missing in the field of construction industry. In this context, this research proposed a general applicable RL-model that supports decision-makers in implementing circular principles in the return flow of their assets while minimising the costs.
Literature review has been conducted to obtain a theoretical framework for the model design. The first part of the literature study has provided a framework to prioritise different waste management strategies based on their level of circularity. The second part of the literature study has been conducted to get an insight in the characteristics of the reverse logistics in the infrastructure and how the principles of circular economy can be included in the RL-process.
The most suitable modelling approach for the proposed problem has been found to be linear programming.
The proposed RL-model has been designed as a transhipment model and is based on mixed integer linear programming (MILP). The proposed model design simulates the reverse flow of one asset moving from a deconstruction project, through the processing facilities to the final redistribution. The proposed model has been complemented by a guideline that supports the application of the model for specific cases.
To test the applicability of the model and the proposed guideline, the model has been applied on two different types of assets in the infrastructure: concrete and street lights.
The model for both cases have been run for multiple scenarios, based on the developments in the industry. The results of the model provides two types of information. Firstly, it provides a trade-off between the level of circularity and the costs. Secondly, it gives the corresponding network-design for the different outcomes. It was found that technical innovations in combination with efficient transportation will help to create return flow in the infrastructure, according to the principles of circular economy, which is financially feasible.
Overall the model has been proven to be general applicable and it supports decision-makers in making decisions about the reverse flow. The outcome gives information about the financial impact on implementing circular principles. The corresponding RL-network provides an insight in which aspects in the RL-process influences the outcome.

Professionals in the Operations and Maintenance phase of national road
infrastructure projects are making decisions with large consequences based on low frequency measurements and subjectivity prone expert observations. A solution is expected in sensor innovations, IoT and User Generated Data to generate more frequent measurements and less subjective observations. The use of these methods has been researched and proven, however the main focus of these studies was often improvement of the technological capabilities or implementation in current practises with limited research into their contribution and effects on professionals in the construction sector. This research describes an experiment that tested the effects of more data and more diverse data on the decision-making of professionals in the construction industry. An attempt to answer this question is made by modelling different data sources into a Serious Game and testing the assumptions. After analysis of the gaming data, the questionnaires and the debriefing it can be concluded that in this experiment there was a correlation between better assessments and higher scores. However experts did not assess damage differently when presented with extra information, nor did they make significantly different decisions. From the qualitative section of the experiment the explanation was found that the extra information proved too much and experts were able to extrapolate with the marginal data that represents the current industry practise. This suggests that new information requires training, as our built environment gets richer in terms of data, the assessment of this built environment becomes too much for humans to cope with and solutions can be sought in the application smart algorithms, Machine Learning and Artificial Intelligence.