Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- PART I INTRODUCTION
- PART II INFRASTRUCTURE SYSTEMS
- 2 Infrastructure Systems
- 3 Disruptions
- 4 Graphs and Networks
- 5 Big Data and Resilience Engineering
- 6 Graphical Models
- 7 Belief Functions
- 8 Tensors Applications
- 9 Resilience Index—Selected Examples
- 10 Epilogue
- Index
- References
3 - Disruptions
from PART II - INFRASTRUCTURE SYSTEMS
Published online by Cambridge University Press: 05 March 2016
- Frontmatter
- Contents
- Preface
- Acknowledgments
- PART I INTRODUCTION
- PART II INFRASTRUCTURE SYSTEMS
- 2 Infrastructure Systems
- 3 Disruptions
- 4 Graphs and Networks
- 5 Big Data and Resilience Engineering
- 6 Graphical Models
- 7 Belief Functions
- 8 Tensors Applications
- 9 Resilience Index—Selected Examples
- 10 Epilogue
- Index
- References
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.
- Type
- Chapter
- Information
- Resilience EngineeringModels and Analysis, pp. 43 - 65Publisher: Cambridge University PressPrint publication year: 2016