Abstract
Optimizing the post-disaster recovery of damaged infrastructure systems is essential to alleviate the adverse impacts of natural disasters to community and enhance their disaster resilience. Post-disaster infrastructure recovery planning aims at achieving efficient and effective recovery of the already damaged infrastructure systems. As a result of infrastructure interdependencies, the complete functional restoration of a facility in one infrastructure system relies on not only the physical recovery of itself, but also the recovery of the facilities in other systems that it depends on. This study introduces the Interdependent Infrastructure Recovery Planning (IIRP) problem, which aims at optimizing the assignment and scheduling of the repair teams for an infrastructure system with considering the repair plan of the other infrastructure systems during the post-disaster recovery phase. Key characteristics of the IIRP problem are identified and a game theory-based IIRP decision framework is presented. Two recovery time-based performance metrics, the total facility recovery waiting time and total service restoration waiting time are introduced and applied to evaluate the efficiency and effectiveness of the post-disaster recovery plan. The IIRP decision framework is illustrated using the interdependent power and water systems of the Centerville virtual community subjected to seismic hazard.
Original language | English (US) |
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State | Published - 2019 |
Event | 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019 - Seoul, Korea, Republic of Duration: May 26 2019 → May 30 2019 |
Conference
Conference | 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019 |
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Country/Territory | Korea, Republic of |
City | Seoul |
Period | 5/26/19 → 5/30/19 |
Keywords
- Decision-making
- Dynamic Integrated Network model
- Game theory
- Infrastructure recovery optimization
- Interdependency
ASJC Scopus subject areas
- Civil and Structural Engineering
- Statistics and Probability