Eujeong Choi

PhD Candidate
Department of Civil & Environmental Engineering

Seoul National University

Eujeong Choi is currently a PhD Student at the Department of Civil & Environmental Engineering at the Seoul National University. Her main research topic is Urban Community and Infrastructure Network level Disaster-Hazard Resilience Planning. More specifically, her work identifies the vulnerable neighborhood in the urban community and critical component in the infrastructure network in terms of disaster-hazard resilience, which support the pre- and post-disaster resilience planning. She received the student paper award during the International Symposium on Life-cycle Engineering and Sustainability of Infrastructure (ISLESI 2017), and was the recipient of the "Young Researchers Exchange Program between Korea and Switzerland 2016-2017" funded by the Swiss State Secretariat for Education, Research and Innovation (SERI).

Disaster Resilience Planning for the Urban Community and Infrastructure Networks

Under natural disaster events, urban communities may suffer from devastating and undesirable losses due to their high population density and complex interdependency. To mitigate such losses effectively, it is essential to identify vulnerable zones in the urban community and critical post-disaster scenarios for infrastructure network in terms of disaster resilience.

 

[Quantitative Assessment of Urban Disaster Resilience by Clustering Analysis of Vulnerability and Recoverability]

 

Since disaster resilience is a multidimensional concept, resilience assessment needs to involve measuring both physical and social capabilities of the community against disasters. To date, in both engineering and social science areas, researchers have made various efforts to quantify resilience in each perspective. However, there have been rare attempts to comprehensively measure disaster resilience because physical and social resources of a community have fundamentally different nature and complex relationships, and thus make the quantitative assessment challenging. To overcome the challenge, the clustering-based method for effective assessment of the disaster resilience of an urban community is proposed. The spatial distributions of multiple factors influencing the vulnerability and recoverability of the community are first identified using a GIS-based disaster impact analysis platform. Based on the results, the vulnerability and recoverability measures are computed at each sub-area. By clustering analysis of these measures, each sub-area is classified in terms of the disaster resilience characteristics. The proposed methodology is expected to broaden understanding of disaster resilience of an urban community and thus support decision making processes for enhancing disaster resilience.

 

[Identification of Critical Post-Disaster Scenarios for Infrastructure Networks using Multi Objective Genetic Algorithm]

 

In risk management of infrastructure networks, finding critical post-disaster scenarios of component failures is essential to support decision making processes regarding retrofits and emergency recovery strategy within limited resources. To build and maintain a resilient infrastructure network, such critical scenarios should be selected based on their socioeconomic impact. Due to the complexity of the problem, a heuristic approach using Multi-Objective Genetic Algorithm (MOGA), can be used to identify such critical scenarios. In order to inherent characteristics of genetic algorithms and complexity of the problem, however, non-dominated solutions may lose the diversity as the size of the network increases. To overcome this issue, a Multi-Group Non-dominated Sorting Genetic algorithm (MG-NSGA) is proposed. Multiple decision objectives, which could reflect the socioeconomic effects on community, are considered in the search process for critical scenarios. Eventually, the proposed approach would support various decision-making processes to build and maintain resilient infrastructure systems.