Sustainable and Resilient Critical Infrastructure Systems

National security, economic prosperity, and the quality of life of today’s societies depend on the continuous and reliable operation of interdependent infrastructures. Models to capture the performance and operation of these systems have been developed to support planning, maintenance, and retrofit decision making from multiple view points, including infrastructure owners or investors, private and public users, and government entities that ensure reliability, economic vitality and security. The study of interdependent infrastructures is challenging due to heterogeneous quality and insufficient data availability and the need to account for their spatial and temporal aspects of complex supply-demand operation. Research and implementation studies have attempted to address interdependence modeling through various techniques, such as Agent Based simulation, Input- Output Inoperability, system reliability theory, nonlinear dynamics, and graph theory. These studies are mainly targeted at understanding infrastructure behavior and response to disruptions through single modeling techniques. However, hybrid modeling techniques, multi-scale analyses, and other realizable innovative approaches are lacking, in part because few studies have characterized existing models into a single source to provide a current state of the field, elucidate connections across existing studies, and synthesize a directive for future research. This chapter introduces a conceptualization of current research that integrates the multiple ideas in the field of infrastructure interdependencies into a unified hierarchical structure that navigates through research advances from early papers in the1980’s to date. The body of knowledge is categorized according to several attributes identified in the field, such as mathematical method, modeling objective, scale of analysis, quality and quantity of input data, targeted discipline, and end user type. The hierarchical conceptualization approach synthesizes available data and is expected to ease the research and application process of interdependencies concepts by finding differences and commonalities in data collection, analyses techniques, and desired outputs. This research survey highlights that most of the existing interdependence modeling strategies are not competing but rather complementary approaches, which can provide a vehicle for immediate innovative studies on coupled infrastructures, such as stochastic interdependence, cascading failures across systems, and the establishment of risk mitigation principles. New linkages across existing research can facilitate implementation and dissemination of results, inform areas of data collection, enable benchmark models for validation predictions and model comparisons, and point to long term broader and emergent unresolved research issues in infrastructure interdependence research, possibly including smart technologies, bio-inspiration, sustainability, scalability of analysis algorithms, and dimension reduction of network abstractions.

The most significant energy consuming infrastructures and the greatest contributors to greenhouse gases for any nation today are electric and freight/passenger transportation systems. Technological alternatives for producing, transporting, and converting energy for electric and transportation systems are numerous. Addressing costs, sustainability, and resiliency of electric and transportation needs requires long-term assessment since these capital-intensive infrastructures take years to build with lifetimes approaching a century. Yet, the advent of electrically driven transportation, including cars, trucks, and trains, creates potential interdependencies between the two infrastructures that may be both problematic and beneficial. We are developing modeling capability to perform long-term electric and transportation infrastructure design at a national level, accounting for their interdependencies. The approach combines network flow modeling with a multiobjective solution method. We describe and compare it to the state-of-the-art in energy planning models. An example is presented to illustrate important features of this new approach.

Recent U.S. national mandates are shifting the country’s homeland security policy from one of asset-level critical infrastructure protection (CIP) to allhazards critical infrastructure resilience, creating the need for a unifying framework for assessing the resilience of critical infrastructure systems and the economies that rely on them. Resilience has been defined and applied in many disciplines; consequently, many disparate approaches exist. We propose a general framework for assessing the resilience of infrastructure and economic systems. The framework consists of three primary components: (1) a definition of resilience that is specific to infrastructure systems; (2) a quantitative model for measuring the resilience of systems to disruptive events through the evaluation of both impacts to system performance and the cost of recovery; and (3) a qualitative method for assessing the system properties that inherently determine system resilience, providing insight and direction for potential improvements in these systems.

In this chapter, we present a computable theory of multi-region optimal economic growth that may be used to determine tax policy and public infrastructure investment plans that facilitate financial, labor force, and environmental sustainability. A numerical example is presented to illustrate use of our theory.

Critical infrastructure systems are tightly-coupled socio-technical systems with complicated behavior. They have emerged as an important focal point of research due to both their vital role in the normal conduct of societal activities as well as their inherent appealing complications for researchers. In this chapter, we will report on our experience in developing techniques, tools and algorithms for revealing and interpreting the hidden intricacies of such systems. The chapter will include the description of several of our technologies that allow for the guided understanding of the current status quo of infrastructure systems through the Astrolabe methodology, the formal profiling of infrastructure systems using the UML-CI meta-modeling mechanism, and also observing the emergent behavior of these complex systems through the application of the agent-based AIMS simulation suite.

The visual inspection routines mandated through the National Bridge Inspection Standards (NBIS) implemented after the 1967 catastrophic collapse of the Silver Bridge have nearly exclusively provided the framework for bridge management encompassing rehabilitation planning and reconstruction scheduling. Over the years, despite the numerous revisions of the NBIS to introduce special inspection procedures, such as for fracture critical and scour susceptible structures, it is evident that the visual inspection program falls short of ensuring a safe and efficient operational model for bridge management. All too often, imminent or unforeseen collapse predates reconstruction efforts and consequently the public is subjected to abrupt closures instead of anticipated and expediently scheduled rehabilitation projects. Sensor-based non-destructive condition assessment and anomaly detection technologies have long been proposed to supplement the limitations and subjectivity associated with the visual inspection program to provide timely and quantitative evaluation of the structural health of highway bridges. Presented is an overview of the role that intelligent transportation infrastructure technologies are increasingly assuming within bridge management as well as conceptual strategies for application of several monitoring approaches. Real-world field response measurements are also presented to demonstrate the current capabilities, typical data collection, and extraction of performance parameters from application of a wireless sensing network platform utilizing both strain transducers and accelerometers. Lastly, identification and localization of non-critical damage onset using a network of vibration sensors is explored through system identification and response prediction using forward innovations.

This chapter presents a methodology for maintenance optimization for heterogeneous infrastructure systems, i.e., systems composed of multiple facilities with different characteristics such as environments, materials and deterioration processes. We present a two-stage bottom-up approach. In the first step, optimal and near-optimal maintenance policies for each facility are found and used as inputs for the system-level optimization. In the second step, the problem is formulated as a constrained combinatorial optimization problem, where the best combination of facility-level optimal and near-optimal solutions is identified. An Evolutionary Algorithm (EA) is adopted to solve the combinatorial optimization problem. Its performance is evaluated using a hypothetical system of pavement sections. We find that a near-optimal solution (within less than 0.1% difference from the optimal solution) can be obtained in most cases. Numerical experiments show the potential of the proposed algorithm to solve the maintenance optimization problem for realistic heterogeneous systems.

Natural hazards such as earthquakes, floods and tornadoes can cause extensive failure of critical infrastructures including bridges, water and sewer systems, gas and electricity supply systems, and hospital and communication systems. Following a natural hazard, the condition of structures and critical infrastructures must be assessed and damages have to be identified; inspections are therefore necessary since failure to rapidly inspect and subsequently repair infrastructure elements will delay search and rescue operations and relief efforts. The objective of this work is scheduling structure and infrastructure inspection crews following an earthquake in densely populated metropolitan areas. A model is proposed and a decision support system is designed to aid local authorities in optimally assigning inspectors to critical infrastructures. A combined Particle Swarm - Ant Colony Optimization based framework is developed which proves an instance of a successful application of the philosophy of bounded rationality and decentralized decision-making for solving global optimization problems.

Most of the highway infrastructure elements, such as pavements, bridges, and tunnels deteriorate rapidly. Their upkeep and timely maintenance is critical for driver safety and traffic mobility. Deterioration of highway infrastructure is caused due to routine wear and tear (deterministic), and unanticipated (stochastic) events, such as accidents and adverse weather. The deterministic part is generally modeled using the Markovian assumption which requires inspections to be performed at fixed time intervals. The stochastic aspect of infrastructure deterioration while often encountered in real-world situation, has not been modeled in previous works, primarily due to the computational and modeling complexities. In this paper, we develop formulations and solution algorithms for both deterministic and stochastic aspects of infrastructure deterioration. We discuss the current state of practice adopted for infrastructure maintenance at the City of Baltimore Department of Transportation (BDOT), and examine our approach though an illustrative example for a bridge network example taken from the BDOT.

Sustainable construction technologies for transportation infrastructure promises to provide full and lasting environmental, social and economic benefits to not only present-day users but also future generations. Recycled pavements can be both economically and environmentally sustainable when their structural adequacies meet the requirement. Rubblization of deteriorated concrete highways is considered to be a green pavement recycling technology that is both cost-effective and yields long-lasting performance. This chapter introduces two approaches - Deflection Basin Parameters (DBPs) and a hybrid Shuffled Complex Evolution (SCE)-Artificial Neural Networks (ANN) - to characterize structural condition of rubblized concrete pavements using Non-Destructive Test (NDT) deflection measurements. The utilization of these approaches in real word case scenarios is demonstrated to provide alternative solutions to the complex structural condition assessment problem for sustainable pavements.