Complex Infrastructure Risk Networks: A UNM Framework for Analyzing Systematic Interconnectivity

Large-scale infrastructure projects face significant risk management challenges, mainly because mapping interdependencies among risks is difficult. Conventional methods tend to analyze risks partially or in isolation, failing to capture the dynamics within risk networks influenced by external factors such as funding and regulations. This limitation often leads to inaccurate risk estimations in complex project environments. This research aims to develop the Uncertainty Network Model as a methodological approach to map this systematic interconnectivity. This study explains how the model visualizes non-linear risk interactions through a literature review, semi-structured interviews with industry experts, and an analytical description of the model’s structure. The results show that the Uncertainty Network Model integrates stakeholder knowledge into a network representation that reveals the interconnection pathways between uncertainties. Unlike standard checklist-based approaches, this model supports systemic risk identification by uncovering hidden dependencies among project variables. This enables more targeted mitigation decisions by directing focus toward the root causes and propagation pathways of problems within the project network. In conclusion, this research positions the Uncertainty Network Model as a systemic alternative to traditional risk management. By understanding these interconnectivity pathways, project managers can allocate resources more strategically to improve the likelihood of successful outcomes in complex infrastructure environments.