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.