Network Interdependency Modeling for Risk Assessment on Built Infrastructure Systems

As modern infrastructures become more interconnected, the decision-making process becomes more difficult because of the increased complexity resulting from infrastructure interdependencies. Simulation and network modeling provide a way to understand system behavior as a result of interdependencies. One area within the asset management literature that is not well covered is infrastructure system decay and risks associated with that decay. This research presents an enhanced version of Haimes\u27 input-output inoperability model (IIM) in the analysis of built infrastructure systems. Previous applications of the IIM characterized infrastructure at the national level utilizing large economic databases. This study develops a three-phased approach that takes component level data stored within geographic information systems (GIS) to provide a metric for network interdependency across a municipal level infrastructure. A multi-layered approach is proposed which leverages the layered data structure of GIS. Furthermore, Monte Carlo simulation using stochastic decay estimates shows how infrastructure risk as a result of interdependency effects changes over time. Such an analysis provides insight to infrastructure asset managers on the impact of policy and strategy decision-making regarding the maintenance and management of their infrastructure systems

A Spatial Risk Analysis of Oil Refineries within the United States

A risk analysis methodology is necessary to manage the potential effects of oil refinery outages to the increasingly connected, interdependent critical infrastructure of the United States. Following the terrorist attacks of 9/11, the lack of a critical infrastructure risk mitigation strategy was identified as an area for improvement. In both the 9/11 attacks and Hurricane Katrina, cascading failures occurred due to the interdependencies among infrastructures and their spatial relationships. Furthermore, the U.S. military is dependent on oil refining capability and a major shortage could potentially have devastating effects on mission accomplishment. As a result, a need has emerged to better quantify the risks associated with disasters to critical infrastructure within the United States. Currently, the Department of Homeland Security\u27s risk equation only measures the individual risks associated with the individual parts of an infrastructure system; it does not measure impacts to the entire system. The goal of this study is to establish a process and develop techniques to account for risk to both the critical infrastructure system and the critical components of the system. The study proposes a modified risk equation that incorporates the traditional elements of individual risk and the system elements of risk. The modified equation proposes two additional variables: Spatial Relationship and Coupling Effect. Three scholarly articles are presented to describe the development of these variables and to compare the traditional and modified risk equations. The modified equation has three benefits: the system effects are incorporated into the current equation, the equation provides more fidelity and minimizes additional data, and the additional data is easily executed

Quantifying restoration costs in the aftermath of an extreme event using system dynamics and dynamic mathematical modeling approaches

Extreme events such as earthquakes, hurricanes, and the like, lead to devastating effects that may render multiple supply chain critical infrastructure elements inoperable. The economic losses caused by extreme events continue well after the emergency response phase has ended and are a key factor in determining the best path for post-disaster restoration. It is essential to develop efficient restoration and disaster management strategies to ameliorate the losses from such events. This dissertation extends the existing knowledge base on disaster management and restoration through the creation of models and tools that identify the relationship between production losses and restoration costs. The first research contribution is a system dynamics inoperability model that determines inputs, outputs, and flows for roadway networks. This model can be used to identify the connectivity of road segments and better understand how inoperability contributes to economic consequences. The second contribution is an algorithm that integrates critical infrastructure data derived from bottom-up cost estimation technique as part of an object-oriented software tool that can be used to determine the impact of system disruptions. The third contribution is a dynamic mathematical model that establishes a framework to estimate post-disaster restoration costs from a whole system perspective. Engineering managers, city planners, and policy makers can use the methodologies developed in this research to develop effective disaster planning schemas and to prioritize post-disaster restoration operations --Abstract, page iv

Modelling of the Western University Campus Electrical Network for Infrastructural Interdependencies in a Disaster Response Network Enables Platform

The interdependencies that exist between multiple infrastructures can cause unexpected system behaviour when their component failure occurs due to large disruptions such as earthquake or Tsunami. The complexities of these interdependencies make it very difficult to effectively recover infrastructure because of the several challenges encountered. To overcome these challenges, a research program called Disaster Response Network Enabled Platform (DR-NEP) was initiated. This thesis deals with the modelling of electrical networks in order to study critical infrastructures interdependencies as a part of DR-NEP project. In first module of the thesis, the concept and understanding of interdependencies is presented. For studying the infrastructural interdependencies, three infrastructures are selected at Western campus: electrical power system, steam system and water systems. It is demonstrated that electrical infrastructure is the most significant infrastructure as all other infrastructures are dependent on electrical input. This thesis subsequently presents the development of a detailed model of the electrical power system of Western campus. This model is validated with actual measured data provided by the Western facilities management for different loading conditions and different feeder positions. Such a model has been developed for the first time at Western University. This model can be used not just for studying disaster scenarios but also for planning of future electrical projects and expansion of facilities in the Western campus. The second module of thesis deals with the different disaster scenarios, critical subsystems and the impact of appropriate decision making on the overall working of the Western campus, with a special focus on electrical power systems. The results from the validated electrical model are incorporated into the infrastructural interdependency software (I2Sim). A total of six disaster scenarios are studied; three involving the electrical power systems in collaboration with water and steam systems, and other three involving only the electrical power system. The study of interdependency during disasters is performed to generate a wiser decision making process. The results presented in this thesis are an important addition to the earlier work done in DRNEP project, which only involved three infrastructures: steam, condensate return, and water. In this iv thesis, the information on electrical networks which was earlier missing is provided through the validated electrical power model. It is demonstrated that decisions to reduce electrical power consumption on campus by evacuating campus areas are effective in stabilizing the hospital operations but not in maintaining Western business continuity. A decision to accommodate hospital activities according to power availability appears to be the better choice. The results presented in this thesis will help in a much better manner to pre-plan different preparedness strategies to deal with any future potential emergencies in the Western campus

The American Multi-modal Energy System: Model Development with Structural and Behavioral Analysis using Hetero-functional Graph Theory

In the 21st century, infrastructure is playing an ever greater role in our daily lives. Presidential Policy Directive 21 emphasizes that infrastructure is critical to public confidence, the nation\u27s safety, and its well-being. With global climate change demanding a host of changes across at least four critical energy infrastructures: the electric grid, the natural gas system, the oil system, and the coal system, it is imperative to study models of these infrastructures to guide future policies and infrastructure developments. Traditionally these energy systems have been studied independently, usually in their own fields of study. Therefore, infrastructure datasets often lack the structural and dynamic elements to describe the interdependencies with other infrastructures. This thesis refers to the integration of the aforementioned energy infrastructures into a singular system-of-systems within the context of the United States of America as the American Multi-modal Energy System (AMES). This work develops an open-source structural and behavioral model of the AMES using Hetero-functional Graph Theory (HFGT), a data-driven approach, and model-based systems engineering practices in the following steps. First, the HFGT toolbox code is made available on GitHub and advanced to produce HFGs of systems on the scale of the AMES using the languages Python and Julia. Second, the analytical insights that HFGs can provide relative to formal graphs are investigated through structural analysis of the American Electric Power System which demonstrates how HFGs are better equipped to describe changes in system behavior. Third, a reference architecture of the AMES is developed, providing a standardized foundation to develop future models of the AMES. Fourth, the AMES reference architecture is instantiated into a structural model from which structural properties are investigated. Finally, a physically informed Weighted Least Squares Error Hetero-functional Graph State Estimation analysis of the AMES\u27 socio-economic behavior is implemented to investigate the behavior of the AMES with asset level granularity. These steps provide a reproducible and reusable structural and behavioral model of the AMES for guiding future policies and infrastructural developments to critical energy infrastructures

Quantification of Lifeline System Interdependencies after the 27 February 2010 Mw 8.8 Offshore Maule, Chile, Earthquake

Data on lifeline system service restoration is seldom exploited for the calibration of performance prediction models or for response comparisons across systems and events. This study explores utility restoration curves after the 2010 Chilean earthquake through a time series method to quantify coupling strengths across lifeline systems. When consistent with field information, cross-correlations from restoration curves without significant lag times quantify operational interdependence, whereas those with significant lags reveal logistical interdependence. Synthesized coupling strengths are also proposed to incorporate cross-correlations and lag times at once. In the Chilean earthquake, coupling across fixed and mobile phones was the strongest per region followed by coupling within and across telecommunication and power systems in adjacent regions. Unapparent couplings were also revealed among telecommunication and power systems with water networks. The proposed methodology can steer new protocols for post-disaster data collection, including anecdotal information to evaluate causality, and inform infrastructure interdependence effect prediction models

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Development of a decision support system through modelling of critical infrastructure interdependencies : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Critical Infrastructure (CI) networks provide functional services to support the wellbeing of a community. Although it is possible to obtain detailed information about individual CI and their components, the interdependencies between different CI networks are often implicit, hidden or not well understood by experts. In the event of a hazard, failures of one or more CI networks and their components can disrupt the functionality and consequently affect the supply of services. Understanding the extent of disruption and quantification of the resulting consequences is important to assist various stakeholders' decision-making processes to complete their tasks successfully. A comprehensive review of the literature shows that a Decision Support System (DSS) integrated with appropriate modelling and simulation techniques is a useful tool for CI network providers and relevant emergency management personnel to understand the network recovery process of a region following a hazard event. However, the majority of existing DSSs focus on risk assessment or stakeholders' involvement without addressing the overall CI interdependency modelling process. Furthermore, these DSSs are primarily developed for data visualization or CI representation but not specifically to help decision-makers by providing them with a variety of customizable decision options that are practically viable. To address these limitations, a Knowledge-centred Decision Support System (KCDSS) has been developed in this study with the following aims: 1) To develop a computer-based DSS using efficient CI network recovery modelling algorithms, 2) To create a knowledge-base of various recovery options relevant to specific CI damage scenarios so that the decision-makers can test and verify several ‘what-if’ scenarios using a variety of control variables, and 3) To bridge the gap between hazard and socio-economic modelling tools through a multidisciplinary and integrated natural hazard impact assessment. Driven by the design science research strategy, this study proposes an integrated impact assessment framework using an iterative design process as its first research outcome. This framework has been developed as a conceptual artefact using a topology network-based approach by adopting the shortest path tree method. The second research outcome, a computer-based KCDSS, provides a convenient and efficient platform for enhanced decision making through a knowledge-base consisting of real-life recovery strategies. These strategies have been identified from the respective decision-makers of the CI network providers through the Critical Decision Method (CDM), a Cognitive Task Analysis (CTA) method for requirement elicitation. The capabilities of the KCDSS are demonstrated through electricity, potable water, and road networks in the Wellington region of Aotearoa New Zealand. The network performance has been analysed independently and with interdependencies to generate outage of services spatially and temporally. The outcomes of this study provide a range of theoretical and practical contributions. Firstly, the topology network-based analysis of CI interdependencies will allow a group of users to build different models, make and test assumptions, and try out different damage scenarios for CI network components. Secondly, the step-by-step process of knowledge elicitation, knowledge representation and knowledge modelling of CI network recovery tasks will provide a guideline for improved interactions between researchers and decision-makers in this field. Thirdly, the KCDSS can be used to test the variations in outage and restoration time estimates of CI networks due to the potential uncertainty related to the damage modelling of CI network components. The outcomes of this study also have significant practical implications by utilizing the KCDSS as an interface to integrate and add additional capabilities to the hazard and socio-economic modelling tools. Finally, the variety of ‘what-if’ scenarios embedded in the KCDSS would allow the CI network providers to identify vulnerabilities in their networks and to examine various post-disaster recovery options for CI reinstatement projects

Understanding cognitive differences in processing competing visualizations of complex systems

Node-link diagrams are used represent systems having different elements and relationships among the elements. Representing the systems using visualizations like node-link diagrams provides cognitive aid to individuals in understanding the system and effectively managing these systems. Using appropriate visual tools aids in task completion by reducing the cognitive load of individuals in understanding the problems and solving them. However, the visualizations that are currently developed lack any cognitive processing based evaluation. Most of the evaluations (if any) are based on the result of tasks performed using these visualizations. Therefore, the evaluations do not provide any perspective from the point of the cognitive processing required in working with the visualization. This research focuses on understanding the effect of different visualization types and complexities on problem understanding and performance using a visual problem solving task. Two informationally equivalent but visually different visualizations - geon diagrams based on structural object perception theory and UML diagrams based on object modeling - are investigated to understand the cognitive processes that underlie reasoning with different types of visualizations. Specifically, the two visualizations are used to represent interdependent critical infrastructures. Participants are asked to solve a problem using the different visualizations. The effectiveness of the task completion is measured in terms of the time taken to complete the task and the accuracy of the result of the task. The differences in the cognitive processing while using the different visualizations are measured in terms of the search path and the search-steps of the individual. The results from this research underscore the difference in the effectiveness of the different diagrams in solving the same problem. The time taken to complete the task is significantly lower in geon diagrams. The error rate is also significantly lower when using geon diagrams. The search path for UML diagrams is more node-dominant but for geon diagrams is a distribution of nodes, links and components (combinations of nodes and links). Evaluation dominates the search-steps in geon diagrams whereas locating steps dominate UML diagrams. The results also show that the differences in search path and search steps for different visualizations increase when the complexity of the diagrams increase. This study helps to establish the importance of cognitive level understanding of the use of diagrammatic representation of information for visual problem solving. The results also highlight that measures of effectiveness of any visualization should include measuring the cognitive process of individuals while they are doing the visual task apart from the measures of time and accuracy of the result of a visual task

Risk Quadruplet: Integrating Assessments of Threat, Vulnerability, Consequence, and Perception for Homeland Security and Homeland Defense

Risk for homeland security and homeland defense is often considered to be a function of threat, vulnerability, and consequence. But what is that function? And are we defining and measuring these terms consistently? Threat, vulnerability, and consequence assessments are conducted, often separately, and data from one assessment could be drastically different from that of another due to inconsistent definitions of terms and measurements, differing data collection methods, or varying data sources. It has also long been a challenge to integrate these three disparate assessments to establish an overall picture of risk to a given asset. Further, many agencies conduct these assessments and there is little to no sharing of data, methodologies, or results vertically (between federal, state, and local decision-makers) or horizontally (across the many different sectors), which results in duplication of efforts and conflicting risk assessment results. Obviously, risk is a function of our perceptions and those perceptions can influence our understanding of threat, vulnerability, and consequence. Some assessments rely on perceptions (elicited from subject matter experts) in order to qualify or quantify threat, vulnerability, and consequence. Others exclude perception altogether, relying on objective data, if available. Rather than fault the subjectivity of our perceptions, or muddle objective assessments with personal opinions, it makes sense to embrace our perceptions, but segregate them as a unique component of risk. A risk quadruplet is proposed to systematically collect and integrate assessments of threat, vulnerability, consequence, and perception, such that each dimension can be explored uniquely, and such that all four components can be aggregated into an overall risk assessment in a consistent, transparent, traceable, and reproducible manner. The risk quadruplet draws from the fields of homeland security, homeland defense, systems engineering, and even psychology to develop a model of risk that integrates all four assessments using multicriteria decision analysis. The model has undergone preliminary validation and has proven to be a viable solution for ranking assets based on the four proposed components of risk