Iris Tien

I-Corps: Software for Optimized Infrastructure Asset Management, Repair, and Recovery

Funding Agency: National Science Foundation (NSF) Award #1712613

Role: PI

NSF Org: IIP Division of Industrial Innovation & Partnerships
Award Instrument: Standard Grant
NSF Program(s): I-CORPS
Program Element Code(s): 8023

Program Manager:

Steven Konsek
IIP Division of Industrial Innovation & Partnerships
ENG Directorate for Engineering

Investigator(s):

Iris Tien (Principal Investigator)

Sponsor:

Georgia Tech Research Corporation
Office of Sponsored Programs
Atlanta, GA 30332-0420 (404)894-4819

Abstract:

The broader impact/commercial impact of this I-Corps project is to provide a solution for prioritizing investment in management and retrofit of critical infrastructure. Critical infrastructure systems include water, power, transportation, communication, and fuel networks. These systems are interconnected and interdependent. They are also aging and subject to increasing hazards, with billions of dollars of investment required to modernize, repair, and retrofit these systems. This project will identify the critical components of these systems such that the resources invested have the greatest impact in mitigating the effects of natural disasters and human attacks on infrastructure, including minimizing the potential for cascading failures across multiple systems given an event. It will improve the performance of these systems for both daily operations and disaster response to provide critical infrastructure services to communities.

This I-Corps project is based on previous work in interdependent infrastructure systems modeling. Specifically, a novel probabilistic framework has been developed to model the complex interdependencies between critical infrastructures. This captures the effects of events, outages, and component failures across multiple systems. It includes uncertainty modeling to perform risk and vulnerability analyses. The I-Corps technology is a generalized software tool based on this research that accounts for interdependencies, is developed for both normal operations and unexpected disruptions, and is suitable to any infrastructure system. Compared to current systems that rely on experience or subjective judgment, and only consider individual systems, the technology enables infrastructure owners and system managers to consider impacts across systems to optimize asset management, repair, and recovery decisions to improve infrastructure performance.