Chopra, Shauhrat S.
(2015)
Graph Theoretic Approaches to Understand Resilience of Complex Systems.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Modern society is critically dependent on a network of complex systems for almost every social and economic function. While increasing complexity in large-scale engineered systems offer many advantages including high efficiency, performance and robustness, it inadvertently makes them vulnerable to unanticipated perturbations. A disruption affecting even one component may result in large cascading impacts on the entire system due to high interconnectedness. Large direct and indirect impacts across national and international boundaries of natural disasters like Hurricane Katrina, infrastructure failures like the Northeast blackout, epidemics like the H1N1 influenza, terrorist attacks like the 9/11, and social unrests like the Arab Spring are indicative of the vulnerability associated with growing complexity. There is an urgent need for a quantitative framework to understand resilience of complex systems with different system architectures. In this work, a novel framework is developed that integrates graph theory with statistical and modeling techniques for understanding interconnectedness, interdependencies, and resilience of distinct large-scale systems while remaining cognizant of domain specific details. The framework is applied to three diverse complex systems, 1) Critical Infrastructure Sectors (CIS) of the U.S economy, 2) the Kalundborg Industrial Symbiosis (KIS), Denmark and 3) the London metro-rail infrastructure. These three systems are strategically chosen as they represent complex systems of distinct sizes and span different spatial scales.
The framework is utilized for understanding the influence of both network structure level properties and local node and edge level properties on resilience of diverse complex systems. At the national scale, application of this framework on the U.S. economic network reveals that excessive interconnectedness and interdependencies among CIS significantly amplify impacts of targeted disruptions, and negatively influence its resilience. At the regional scale, analysis of KIS reveals that increasing diversity, redundancy, and multi-functionality is imperative for developing resilient and sustainable IS systems. At the urban scale, application of this framework on the London Metro system identifies stations and rail connections that are sources of functional and structural vulnerability, and must be secured for improving resilience. This framework provides a holistic perspective to understand and propose data-driven recommendations to strengthen resilience of large-scale complex engineered systems.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
11 September 2015 |
| Date Type: |
Publication |
| Defense Date: |
2 June 2015 |
| Approval Date: |
11 September 2015 |
| Submission Date: |
26 July 2015 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
203 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Civil and Environmental Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Resilience; Complex Systems; Critical Infrastructures; Input-Output model; Graph Theory; Network Analysis; Industrial Symbiosis; Transportation systems; London metro-rail system; U.S. Economic network; Sustainability; Disaster preparedness; Disaster management |
| Date Deposited: |
11 Sep 2015 16:01 |
| Last Modified: |
15 Nov 2016 14:29 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/25797 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[img]](https://d-scholarship.pitt.edu/style/images/fileicons/application_pdf.png)
![[feed]](/images/feed-icon-32x32.png){kind=icon}