CRISP Type 2/Collaborative Research: Probabilistic Resilience Assessment of Interdependent Systems (PRAISys)
For additional information refer to the main project website.
Florida Atlantic University
Diana Mitsova-Boneva, Principal Investigator, CMMI-1541089, Associate Professor of urban and regional planning
Alka Sapat, Associate Professor of public administration
Georgia State University
Ann-Margaret Esnard, Professor of public management and policy
Paolo Bocchini, Principal Investigator, CMMI-1541177, Assistant Professor of civil and environmental engineering
Brian Davison, Associate Professor of computer science and engineering
Alberto Lamadrid, Assistant Professor of economics
Richard Sause, Joseph T. Stuart Professor of structural engineering
Lawrence Snyder, Associate Professor of industrial and systems engineering
Wenjuan Sun, Research Associate of civil engineering
Project Period: 09/03/2015 – 09/02/2018
After a disruptive extreme event, such as an earthquake or severe storm, the socio-economic recovery of the affected region depends on the recovery of its infrastructure systems. Lifelines, such as power and water distribution systems, transportation networks, communication systems, and critical buildings have a primary role in disaster response, management, and long-term recovery. The failure to rapidly restore the services required for personal, social, and commercial activities leads to continued socio-economic losses and progressive depopulation. This collaborative project brings together scholars in Civil Engineering, Systems Engineering, Computer Science, Economics, Urban Planning, and Policy Making. Its purpose is to establish and demonstrate a comprehensive framework that combines models of individual infrastructure systems with models of their interdependencies for the assessment of interdependent infrastructure system resilience for extreme events under uncertainty. The “PRAISys” platform (Probabilistic Resilience Assessment of Interdependent Systems) will emphasize a probabilistic approach that permeates all aspects of the models, including the interdependencies. Some types of uncertainties that were not considered before (e.g., the possibility of using contingency plans that provide services without functioning infrastructure) will be classified; while mathematical and computational tools will be devised to capture their characteristics. PRAISys will enable better management and design of next generation infrastructure, more resilient to extreme events and to component failures under normal conditions. This will reduce the likelihood of extreme events becoming catastrophic in terms of casualties and injuries, long-lasting socio-economic losses, and environmental impact. The results of the research will be disseminated to the public in various forms: through series of seminars for professionals and administrators; by participating in Lehigh University’s STAR academy program for disadvantaged middle and high school students; through scientific publications and presentation; and by curriculum development.
The development, calibration, and validation of PRAISys will enable research on stochastic interdependencies among infrastructure systems in the wake of an extreme event. This requires advancements in several disciplines. For instance, a new hybrid reliability model, which combines graph theory for network analysis and classic system reliability to model the probabilistic dependencies among infrastructures will be studied. The new concept of “uncertain dependencies,” which are rigorously modeled and include “contingency plans” will be introduced. Advancements in stochastic network optimization will be sought, to predict the optimal strategies and to inform the disaster management. Social network data will be used as an additional source of information on the recovery of a region, in real time, mining public posts. A comprehensive decision framework will combine the results of the simulation platform with expert opinions and surveys to identify the importance of various aspects of recovery. Finally, new techniques for the collection of large sets of data from utility companies, local government, and other authorities will be studied.
Funding Agency: CMMI Division, National Science Foundation
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