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

Infastructure Interdependencies Modeling and Analysis - A Review and Synthesis

The events of 9/11 and the occurrence of major natural disasters in recent years has resulted in increased awareness and renewed desire to protect critical infrastructure that are the pillars to maintaining what has become normal life in our economy. The problem has been compounded because the increased connectedness between the various sectors of the economy has resulted in interdependencies that allow for problems and issues with one infrastructure to affect other infrastructures. This area is now being investigated extensively after the Department of Homeland Security (DHS) prioritized this issue. There is now a vast extant of literature in the area of infrastructure interdependencies and the modeling of it. This paper presents a synthesis and survey of the literature in the area of infrastructure interdependency modeling methods and proposes a framework for classification of these studies. The framework classifies infrastructure interdependency modeling and analysis methods into four quadrants in terms of system complexities and risks. The directions of future research are also discussed in this paper

Behaviour Analysis of Interdependent Critical Infrastructure Components upon Failure

Urban life increasingly depends on intact critical infrastructures (CIs). For this reason, protecting critical infrastructure systems from natural disasters and man-made hazards has become an important topic in urban development research in recent years as a prerequisite for building and optimizing smart cities. To increase efficiency, the connections between CIs have been strengthened increasingly, resulting in highly interdependent large-scale infrastructure systems that are vulnerable to cascading failures. Hence, studying the cascading and feedback effects caused by the failure of a CI component in a given system can help strengthen this system. Understanding the response of the system in the event of a disaster can lead to better disaster management and better planning of critical infrastructures in the future. The population heavily depends on water, electricity, and the transportation network. These three components also depend on each other to function individually. This complex nature of interdependencies must be studied in order to understand the effects induced in one system due to the failure of another. The three systems (water, transport, and electricity) and their interdependencies can be modeled using graph theory. Water, transport, and electricity networks can be further broken down into smaller components. For example, the water network comprises water treatment plants, water storage tanks, pumping stations, sewage treatment, etc. interdependency factors into the model when, for instance, a pumping station depends on electricity. Graph theory can be used to depict the pairwise relationship between the individual components. Each node in the graph represents a critical infrastructure and the edges between these critical infrastructures represent their dependency. The modeled graph is a multigraph (inter-network dependency) and multidirectional (mutual dependence of two or more components). The idea behind building this model is to simulate the response of the interdependent systems upon failure. Building a simulation tool with an underlying interdependency graph model can not only help in understanding the failure response, but can also help in building a robust system for preserving the infrastructures. The data obtained from the simulation results will contribute to a better emergency response in the event of a disaster. The failure response of a system depends largely on the failed component. Hence, three cases are considered to simulate and identify the state of the system upon failure of a component: The failed component can be a node with maximum outward dependencies, a node with maximum inward dependencies, or a random failure of a component. If a component has the maximum number of outward edges, the simulation tool will help visualize the cascading effects, whereas a system with the maximum number of incoming edges will contribute to the understanding of the feedback response as the outward nodes are not affected immediately. Another goal of CI failure analysis is to develop an algorithm for the partial restoration of specific critical services when a CI is not working at full capacity. The selection of critical infrastructure components for restoration is based on the number of people being affected

Quantifying economic benefits for rail infrastructure projects

Investment in rail infrastructure is necessary to maintain existing service and to cater for future growth in freight and passenger services. Many communities have realized the importance of investment in rail infrastructure projects and set up goals and visions to achieve economic development through investing in such projects. Due to limited funds available, communities have to select a single or very few projects from a variety of projects. It is very critical that right projects must be selected at the right time for a community to realize economic development. The limited methods for quantifying the economic benefits to the stakeholders often cause a problem in the selection process. Most of the conventional methods focus mainly on the economic impact of the project and ignore the metrics that convey the economic impacts in meaningful ways to the key stakeholders involved. This leads to uncertainty in the project selection and planning process and often leads to failure in achieving the goals of the project. This study aims to provide a mathematical framework that quantifies economic benefits of investment in rail infrastructure projects in meaningful ways to the key stakeholders through three different approaches, namely, Leontief-based approach, Bayesian approach and system dynamics approach. The Leontief-based approach is the easiest of all the three approaches provided that historical data is available. Bayesian approach is also very beneficial as it can be used by coupling small data with surveys and interviews. Also, system dynamics model is very useful to conduct qualitative analysis, but the quantitative analysis part can become very complex --Abstract, page iii

Quantifying Economic Benefits for Rail Infrastructure Projects

This project identifies metrics for measuring the benefit of rail infrastructure projects for key stakeholders. It is important that stakeholders with an interest in community economic development play an active role in the development of the rail network. Economic development activities in both rural and urban settings are essential if a nation is to realize growth and prosperity. Many communities have developed goals and visions to establish an economic development program, but they often fail to achieve their goals due to uncertainties during the project selection and planning process. Communities often select a project from a vast pool of ideas with only limited capital available for investment. Selecting the right project at the right time becomes imperative for economic and community development. This process is significantly hampered by limited methods for quantifying the economic benefit to key stakeholders. Four methodologies are used in this project to determine the most useful tools for quantifying benefit given the availability of data, relevant expertise, and other information