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PART II - INFRASTRUCTURE SYSTEMS
- Nii O. Attoh-Okine, University of Delaware
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- Resilience Engineering
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Preface
- Nii O. Attoh-Okine, University of Delaware
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- Resilience Engineering
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Summary
Resilience engineering is becoming a new paradigm for complex systems performance and maintenance decision making. The concept of resilience was introduced by Holling (1973) in the field of ecology and has been well-documented in ecological and social literature and in some management cases. The initial definition of resilience is that it determines the persistence of relationships within systems and is a measure of the ability of these systems to absorb change of state variables, driving variables, and parameters and still persist. Other definitions include “the potential of particular configuration of a system to maintain its structure/function in the face of disturbance, the ability of the system to reorganize following disturbance-driven change and measured by size of stability domain,” and “the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity and feedbacks.” More research has been done on the concept of resilience and its applicability to ecological, social, and business systems in comparison to engineered systems.
Resilience engineering represents a major step forward by proposing a completely new vocabulary instead of adding one more concept to an existing lexicon. Although various definitions of resilience exist that are dependent on the subject area, resilience in infrastructure systems and energy systems (including CO2 sequestration) is the ability of the system to recover and adapt to external shocks, including natural, artificial, and technogenic disasters and failure because of poor design.
This can ultimately affect the smooth and efficient operation of systems and may demand a shift of process, strategy, and coordination. Infrastructure systems in most cases are interconnected. Therefore, analyses of the system should consider interdependency properties. Because of both dependencies and interdependencies, there are various types of effects: (1) cascading effect—when disruption in one infrastructure causes disruption in a second; (2) escalating effect—when disruption in one infrastructure exacerbates an independent disruption of a second infrastructure; and (3) common cause effect—when a disruption of two or more infrastructures occurs at the same time. The last is more prevalent during natural disasters. The interactions create a very delicate web between infrastructures as well as feedbacks and complex topologies at different levels. Therefore, it is nearly impossible to analyze the behavior of any infrastructure in isolation of its environment.
3 - Disruptions
- from PART II - INFRASTRUCTURE SYSTEMS
- Nii O. Attoh-Okine, University of Delaware
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Summary
Introduction
Infrastructure networks can experience both component failures and disruptions. The failure or disruption can be accidental or intentional. These incidents can have both economic and human costs. During large disasters, a large number of critical infrastructures are susceptible to damage and disruption. A disaster is usually referred to as an extreme event if it can disrupt a whole system of interdependent infrastructures such as water supply, transportation, telecommunication, and electric power systems. However, a resilient system has the capacity to adapt to the hazard by resisting or changing in order to reach and maintain an acceptable level of functioning. In most cases, this is determined by the degree to which the social system is capable of organizing itself and its functions, based on the experience and lesson from previous disasters (O'Rourke 2007). During disaster one can identify the following categories of causes: (1) Physical destruction of infrastructure network components, (2) disruption in supporting network infrastructure, and in some cases (3) infrastructure network congestions. Furthermore, most critical infrastructure—for example, telecommunication networks—does not have high degree of redundancy (this will be explained in chapter 4, graphs and networks). Also there are interdependencies between critical infrastructures, and some of them lack acceptable levels of resilience under various conditions. For example, electric outages can cause disruption in transportation systems. Furthermore, depending on how self-healing the interacting systems are, there can be varying degrees of costs involved in restoring appropriate functions and serviceability of each system (Townsend and Moss 2005).
Branscomb (2006) discussed three classes of disasters:
• Natural disasters
• Man-made disasters
• Technogenic disasters resulting from human error and failing infrastructure
Impey (2012) groups hazard into (1) natural and (2) societal. Natural hazards include meteorological, geological, geomophic, and hydrological hazards. The metrological hazards include tropical cyclones and tornadoes; geological hazards include earthquakes and volcanic eruptions; geomorphic hazards include landslides and debris flows; and finally hydrological hazards include river flooding, storm surges, and tsunamis.
Societal hazards include political violence, infectious disease pandemics, industrial and transportation accidents, and fraud catastrophes. A major characteristic of societal hazards is that the community or society tends to be organized to deal with those threats. For example, during the September 11, 2001, terrorist attack and Hurricane Katrina in 2015, the U.S. federal government provided the restoration and recovery.
9 - Resilience Index—Selected Examples
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- Nii O. Attoh-Okine, University of Delaware
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5 - Big Data and Resilience Engineering
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- Nii O. Attoh-Okine, University of Delaware
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Summary
Introduction
Big data is about extremely large volumes of data originating from various sources such as databases, audio and video files, millions of sensors, and other systems. The sources of data in some cases provide outputs that are structured, but most are unstructured, semistructured, or poly-structured. Furthermore, these data are streaming in some cases at a high velocity, and the data exposes at a higher speed as it is generated. Figure 5.1 shows the general framework of big data. The main key to the application of the big data paradigm relies heavily on the selection of appropriate data science techniques.
Hu et al. (2014) presented an overview of big data analytics. The authors summarized three definitions of big data:
The attribute definition defines big data technologies as “a new generation of technologies and architectures designed to economically extract value fromvery large volumes of a wide variety of data by enabling high-velocity capture, discovery, and/or analysis” by (Cooper and Mell 2012).
The second definition is more subjective. Big data consists of “data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.” This is based on the Mckinsey report (Manyika et al. 2011).
The final definition is often referred to as the architectural definition. Per this definition, big data is where the data volume, acquisition velocity, or data representation limits the ability to perform effective analysis using traditional relational approaches or requires the use of significant horizontal scaling for different processing (Cooper and Mell 2012).
Table 5.1 shows the comparison between big data and traditional data.
The big data analytics can be grouped into two alternative paradigms that are present in resilience engineering:
Streaming processing—The potential value of data depends on data freshness. Themajor characteristics data arrives in a stream;a continuous and only a limited portion can be stored.
Batch processing—In this application, the data are stored and analyzed later. In some cases, the data are analyzed in subsets.
Table 5.2 compares streaming processing and batch processing.
The development of advanced sensors and information technologies in critical infrastructure monitoring and control has provided a platform for the expansion and growth of data.
6 - Graphical Models
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- Nii O. Attoh-Okine, University of Delaware
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Summary
Introduction
Graphical models are the combination of graph theory, probability theory, and decision theory. They are referred to by different names depending on the application: influence diagrams, Bayesian networks, decision networks, valuation networks, factor graphs, or Markov random fields. This chapter discusses a few of the models that are applicable to data analysis of large-scale infrastructures and hence resilience modeling and inferences. The graphical models are also compact depictions of independence and factorization assumptions of probability density functions (Chai et al. 2011). Graphical models can be directed graphs or undirected graphs. For example, Bayesian networks are directed and acyclic, whereas factor graphs are undirected. Bilmes (2010) presented a general overview of dynamic graphical models. Most of the basic graphical networks discussed in the literature are what are termed static graphical models. The static models compute probabilistic quantities of interest, either exactly or approximately based on the knowledge of the graph structure and set of Markov properties. In the dynamic model, the graph network is partitioned into various sections, and each section of the graph has its own connectivity rules. Graphical models have several properties that make them applicable to various problems where information, even limited, is available. The following are some of the properties:
• They present a method to visualize the structure of the probabilistic model that can be used to design, motivate and in some cases control new models (Bishop 2006).
• They also provide insights into the properties of a model.
• They provide simple mathematical expressions and graphical manipulations of complex problems.
Information required for resilience engineering models and analysis can be represented as shown in Table 6.1.
Pouly (2011) shows how algebraic structure can unify both the information and the inference. The algebraic structure developed can be analyzed effectively using graphical models.
Important Concepts
The section is based heavily on Castillo et al. (1997) and Lauritzen (1996) and presents a more elaborate concept in graph theory applicable to graphical models. A graph G = (V,E), where V is the finite vertices and E is the set of edges.
1 - Introduction
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- Nii O. Attoh-Okine, University of Delaware
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Summary
Introduction
Prelude
The fundamental ideas about sustainability can be summarized as follows: a sustainable system is one that survives or persists both during normal performance and in an extreme event. Nevertheless this has given rise to other questions, including the following: (1) What system or subsystem or characteristics of systems persist? (2) For how long? (3) When do I find out if the system or subsystem or characteristic persisted? Sustainability and sustainable development have been used interchangeably, but sustainable development is the mother concept that ties the three goals of humanity (entitlement to health, wealth, and justice) in one unique concept (O'Riordan and Voisey 1997). Sustainability has been generally associated with the definition by the World Commission on Environment and Development (1987) as development that meets the needs of the present without compromising the ability of future generations to meet their own needs. The concept applies to the integrated system comprising humans and nature (Cabezas et al. 2004). Although the term “sustainability” was originally framed in terms of famine and overpopulation, it has gradually been transferred to ecosystems and natural resources (Marshall and Toffel 2005). This has given rise to more than 100 definitions of sustainability. Therefore, the multiple definitions of sustainability may have rendered the term meaningless (Elkington, undated).
Gibson (2001) presented the following principles of sustainability:
• Human-ecological systems integrity
• Sufficiency and opportunity
• Equity
• Efficiency and throughput reduction: reducing threats and avoiding waste
• Democracy and civility: applying to sustainability in well informed administrative principles
• Precaution: uncertainty and risks
• Immediate and long-term integration: applying principles at once
In some cases the definition can be difficult to operationalize or implement. An attempt has been made to operationalize sustainability in different forms; for example, economy refers to jobs and wealth, environment refers to environmental qualities and natural resources, and social refers to health and social development. Table 1.1 shows sustainability dimensions. Table 1.2 depicts sustainability categories.
• Furthermore, sustainable development is a complex idea that cannot be easily described or simply applied. The major key of sustainable development is that it is an intergenerational phenomenon; therefore timescale is of an essence (Martens 2006). Sustainable development has many disagreements (Kemp and Martens 2007).
8 - Tensors Applications
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- Nii O. Attoh-Okine, University of Delaware
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Summary
Introduction
Large-scale and critical infrastructure monitoring data is usually high dimensional. The structure of this high dimensional data in most cases and conditions can be characterized by a relatively small number of parameters. Reducing the data dimension presents the engineer with different opportunities, including visualization of the intrinsic structure of the data and more efficient data to develop appropriate models, such as prediction. The “flat-world view” of two-way matrix application may be insufficient in making inferences in interdependent infrastructures. In current literature in infrastructure data analysis, most high dimensional data are inappropriately represented, making it very difficult to develop the correct models for further analysis. In some situations, there is a need to analyze simultaneous effects of many features on interdependent infrastructure. The features can also be nonscalar features.
For example, the use of image analysis in infrastructure monitoring requires a new form of data representation in the large-scale civil infrastructure systems. In general, the resilience of an interdependent network once presented as multiple graphs and the adjacency tensor can provide the framework for addressing the resilience of interdependent networks (Figure 8.1).
The multiple networks (graphs) G(V, E(1)E(2) · · ·E(N)) with a vertex set V and an edge sets ﹛E(1), E(2), · · ·E(n)﹜ and, for example, A(n)i j = 1 indicate situations of the presence of a link from vertex i to j with respect to an infrastructure n. Tensors appear to be an appropriate way to represent high dimensional data in large-scale infrastructure and their interdependences. Tensor factorization and decomposition are becoming major tools for large multidimensional data analysis. Factorizing tensors have better advantages than traditional matrix factorization such as uniqueness of the optimal solution, and the decomposition can explicitly take account of the multiway structure of the data. The application of the tensor, apart from addressing the previous shortcomings, will provide a platform for performing data mining applications. Sun et al. (2006) noted that the tensor approach is capable of detecting anomalies in data. The anomaly detection can proceed from the broadest level to a more specific level. Sun et al. (2006) discusses the process of using tensors in computer network modeling, which have some similarities with large civil network infrastructure.
4 - Graphs and Networks
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- Nii O. Attoh-Okine, University of Delaware
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Summary
Introduction
The fundamental concept of graph theory and networks is a graph that provides basic and mathematical representations. Graph theory has been applied to different networks to describe characteristics such as topology, evolution, and dynamic processes and various network interactions. Barrat (2011) grouped network into two classes: (1) the natural systems that compose biological networks (genes, proteins), foodwebs, and social networks; and (2) the infrastructure network, which includes virtual (web) and physical (power grids, transportation) networks. Barrat (2011) also discussed how dynamic processes within a complex network can provide understanding about the resilience and vulnerability of the networks. Newth and Ash (2005) demonstrated through analysis how network properties can provide insight in cascading failures and resilience of large-scale infrastructure networks. Lewis (2009) classified networks as (1) static—when the propagation of nodes, links, and mapping functions remain, unchanged over time;and (2) dynamic—when the number of nodes and links and other information such as mapping functions changes over time. The time varying change leads to reorganization of the network, leading to a phenomenon called emergence. Mathematically, dynamic networks can be expressed as follows:
G(t) = [N(t), L(t), f (t) : R]
This is a time, varying 3-tuple consisting of a set nodes N(t), a set of links L(t), and a mapping function f (t), and R is the microrules that map the network from one state to another. Steen (2010) presented a comprehensive introductory analysis of graph theory and complex networks. Steen presented the basic mathematical principles and proper formulation of graph theory and complex network in real-world applications. Graph theoretic algorithms and metrics can extract useful information, which is the main objective of solving complex systems problems. There are various publications on graph and networks such as Albert and Barabási (2002) and Strogatz (2001), which are comprehensive and present more details. The aim of this chapter is to present a simple overview and provide direction on how the techniques can be applicable to resilience models. The chapter focuses on undirected graph network.
Network analysis presents a unique approach in solving and making inferences about various infrastructure networks. The structure of a critical network can model as a network. The nodes and links abstractly represent bridges and roads, city and railway networks, power generators and power lines or sector assets and relationships among those assets (Lewis 2006).
Index
- Nii O. Attoh-Okine, University of Delaware
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Frontmatter
- Nii O. Attoh-Okine, University of Delaware
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2 - Infrastructure Systems
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Summary
Critical Infrastructure
Infrastructure is defined as physical assets that are capable of an intended service delivery and comprised of rigid assets such as buildings, bridges, and roads and flexible assets such as utilities and facilities related to water, sewage, and power. Critical infrastructure spans across a number of key sectors including energy, finance, and information technology. An infrastructure system is an integrated structured network of interdependent entities that aid in the service delivery capability of rigid and flexible assets.
Infrastructure evolved with society and technology and is a major key in boosting both the economy and living standards of a population. Critical infrastructure systems are therefore built to provide services and, in some cases, jobs for several generations (National Research Council 2009). Critical infrastructure is almost always subject to political and social pressures. The economic pressures in most cases result in delayed maintenance and rehabilitation. Recently, in the face of terrorists and other saboteurs, infrastructures that symbolize a nation's pride—for example, the Statue of Liberty—are also considered as critical infrastructure (Chai et al. 2011).
The Royal Academy of Engineering (2011) extensively discussed the meanings and principles of “smart structure.” It defined smart structure as a system that uses a feed loop of data, providing evidence about the state of the infrastructure for effective maintenance and rehabilitation decision making. Therefore, the system can monitor, measure, analyze, communicate, and react based on information and data captured by multiple sensors. The principles of the smart structure include (1) large data acquisition and collection, (2) complex mathematical modeling for decision making and engineering assessment, (3) feedback systems that control collected information and improve infrastructure system operations, and (4) adaptability. The system is versatile enough to incorporate new communications and other technologies.
There are five criteria used by different nations and jurisdictions to determine critical infrastructures: (1) health, (2) safety, (3) economy, (4) continuous functioning of government, and (5) national/society morals. The following definition of critical infrastructure was presented by the National Research Council (NRC 2002): critical infrastructure systems are defined as the water, wastewater, power, transportation, and telecommunication systems without which buildings, emergency response systems, and other infrastructure cannot operate as intended.
PART I - INTRODUCTION
- Nii O. Attoh-Okine, University of Delaware
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Contents
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10 - Epilogue
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- Nii O. Attoh-Okine, University of Delaware
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Summary
General
Resilience engineering is now a new paradigm that cannot be overlooked in critical infrastructure modeling, control, and decision making. The initial concept was based on the edited work by Hollnagel et al. (2006), which was more focused on human errors, machine failures, and safety critical systems involving humans. Recently, resilience engineering has been referred to as the art of managing the unexpected or how teams or organizations become prepared to cope with surprises. These surprising events can sometimes push the system beyond its operational boundaries (Woods 2006). Therefore, the purpose of resilience engineering is to anticipate the changing potential for failure considering that plans and procedures have limits, gaps, and unforeseen errors and that the environment is very dynamic (Hollnagel et al. 2006).
Networked and lifeline infrastructure appear to be one of the great challenges, especially in the presence of a surprise event. Designing resilient systems can limit and reduce the probability of failure and its consequences. Currently, there are a few metrics for evaluating resilience of standalone infrastructures and their interdependencies, but they are very limited and inconsistent. The most successful metrics are when only two infrastructure systems are interacting. Furthermore, there is no standard or a universal method of developing and analyzing the resilience indices of more than two interacting infrastructures or systems of interdependent networks. Buldyrev et al. (2010) and Leicht and D'Souza (2009) are a few examples of researchers leading the development of consistent and objective methods for analyzing interdependent networks. Resilience engineering is becoming a new paradigm for complex systems performance and maintenance decision making, and the resilience engineering principle fits within the sustainability framework.
Looking Back
Proper formulating and analyzing of resilience engineering problems and applications requires a strong background in graph theory, statistics, and machine learning algorithms. Also resilience indices are time-dependent metrics. The majority of the ideas presented in the book are toward infrastructure systems and general networks. It would be incorrect to use one metric to determine the resilience index of the systems, since for identical systems, a slightly different resilience index under different time conditions; therefore, the resilience index should be used as a guide. For example, in more sophisticated methods developed by physicists, there are many assumptions in the question used that will ultimately affect the final results.
7 - Belief Functions
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- Nii O. Attoh-Okine, University of Delaware
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Acknowledgments
- Nii O. Attoh-Okine, University of Delaware
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Resilience Engineering
- Models and Analysis
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With an eye towards environmental sustainability, this book presents a step-by-step approach to formulating the resilience of civil infrastructure and energy systems. It provides a concise explanation of resilience terminology, a general overview of theoretical models and analyses, and a clear guide to practical applications, covering critical topics such as interdependent infrastructures and geologic carbon sequestration. Additionally, it contains a general introduction to selected data science topics, so readers can acquire the tools to formulate and analyze resilience engineering problems in depth. Informed by the author's extensive practical experience and thorough academic background, this book includes examples to illustrate key computational algorithms, end-of-chapter exercises and references, and an entire chapter devoted to case studies. Intended for practitioners of resilience engineering with a background in probability, this book offers a balanced blend of background, theory, and practical examples to address one of the most important emerging topics of modern times.