Dynamic Analysis of Levee Infrastructure Failure Risk: A Framework for Enhanced Critical Infrastructure Management

Current models that assess infrastructure failure risk are "linear," and therefore, only consider the direct influence attributed to each factor that defines risk. These models do not consider the undeniable relationships that exist among these parameters. In reality, factors that define risk are interdependent and influence each other in a "non-linear" fashion through feedback effects. Current infrastructure failure risk assessment models are also static, and do not allow infrastructure managers and decision makers to evaluate the impacts over time, especially the long-term impact of risk mitigation actions. Factors that define infrastructure failure risk are in constant change.

In a strategic manner, this research proposes a new risk-based infrastructure management framework and supporting system, Risk-Based Dynamic Infrastructure Management System (RiskDIMS), which moves from linear to non-linear risk assessment by applying systems engineering methods and analogs developed to address non-linear complex problems. The approach suggests dynamically integrating principal factors that define infrastructure failure risk using a unique platform that leverages Geospatial Information System services and extensions in an unprecedented manner. RiskDIMS is expected to produce results that are often counterintuitive and unexpected, but aligned to our complex reality, suggesting that the combination of geospatial and temporal analyses is required for sustainable risk-based decision making. To better illustrate the value added of temporal analysis in risk assessment, this study also develops and implements a non-linear dynamic model to simulate the behavior over time of infrastructure failure risk associated with an existing network of levees in New Orleans due to diverse infrastructure management investments. Although, the framework and RiskDIMS are discussed here in the context of levees, the concept applies to other critical infrastructure assets and systems. This research aims to become the foundation for future risk analysis system implementation.