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When a seismic event occurs, transportation networks play a critical role in securing a community’s resilience, from enabling emergency activities to supporting a rebound to normality.
While this makes it crucial to assess their resilience accurately, seismic resilience analysis of transportation networks remains challenging as it involves a large number of and various types of variables, each of which is subjected to its own uncertainty.
The present study addresses this issue by employing Bayesian network (BN), whose visualization of high-dimensional probability distributions facilitates integrating different types of information to perform a probabilistic analysis. Such information includes hazard maps, fragility curves, restoration data, network reliability analysis, resilience measures, and qualitative data.
On the other hand, to effectively serve an analysis purpose of interest, resilience should be evaluated by proper measures and formats. Being a part of the URKI GCRF project ‘Tomorrow’s Cities’, the interest of the present study lies in addressing the issue of disaster inequality in urban environments.
To support such initiative, we evaluate seismic resilience measures at multiple locations over a case study area and visualize them by a map, so that we can investigate spatial variances in disaster resilience. The proposed analysis framework is demonstrated by an application to a transportation network in Istanbul, Turkey.
Postdoctoral researcher at Engineering Risk Analysis Group, Technical University of Munich, Germany
Dr. Ji-Eun Byun specialism lies in reliability analysis and reliability-based optimization of structural and infrastructure systems against extreme events including natural/human-made hazards and structural deteriorations.
She is particularly interested in analyzing complex and large-scale systems such as transportation networks, utility distribution systems and structural systems, for which she has developed novel reliability methods that can handle high-dimensional probability distributions.
Her research also includes an interdisciplinary management of infrastructure systems, for which she has been employing probabilistic graphical models to integrate data and methods from various disciplines, such as engineering, social science, and finance. Through these efforts, she ultimately aims to enhance our community's risk intelligence so that we can effectively design and manage our engineering systems.
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