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- W2969355340 abstract "Identifying critical post-disaster scenarios in terms of their socioeconomic consequences is of the utmost importance for disaster risk management of infrastructure networks. Recently, multi-objective genetic algorithms have been employed to find such critical post-disaster scenarios for complex systems. However, a large size of a network may hamper multi-objective genetic algorithm from obtaining final solutions that are accurate and robust against variability. To overcome this challenge, a Multi-Group Non-dominated Sorting Genetic algorithm (MG-NSGA) is proposed in this study. It is presented in the paper that MG-NSGA can effectively identify critical post-disaster scenarios by improving the diversity of sample populations. Furthermore, a concept of ‘critical zone’ in the solution space is proposed to determine a group of important post-disaster scenarios identified by MG-NSGA. By large-size real infrastructure network examples of EMA highway network and Jeju transportation network, the proposed MG-NSGA-based identification method is successfully demonstrated." @default.
- W2969355340 created "2019-08-29" @default.
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- W2969355340 date "2019-12-01" @default.
- W2969355340 modified "2023-09-26" @default.
- W2969355340 title "Development of Multi-Group Non-dominated Sorting Genetic Algorithm for identifying critical post-disaster scenarios of lifeline networks" @default.
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- W2969355340 doi "https://doi.org/10.1016/j.ijdrr.2019.101299" @default.
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