Complex Infrastructure Systems Analysis and Management, a Case-Study

Abstract

Complex infrastructure systems are difficult to model. Data is often missing or erroneous, changes are not well documented, and processes are not well understood, on top of it, under complexity, stalwart analytical tools have limited predictive power. The aleatoric risk, such as rain and risk cascading from interconnected infrastructures, is unpredictable. Mitigation, response, and recovery efforts are adversely affected. 

In this research, complexity, nonlinear noncontinuous effects and aleatoric and data unknowns, are bypassed by directly addressing systems’ response. Graph theory, statistics, as well as Digital Signal Processing tools are applied within a theoretical framework.

A case of a complex infrastructure system is studied. Faults’ virtual network is generated, statistics, spatio-temporal behavior and traits are produced. The gained understanding is compared to the physical system’s design and to its modus operandi. Implications for design and maintenance are inferred and DSP tools to manage the system in real time are developed.

The contribution of this research is in the provision of an explicatory theory and a management paradigm for complex systems.