Abstract
When a seismic event occurs, transportation networks play a critical role in undertaking emergency activities such as evacuation and relief supply. Accordingly, to secure their functionality, it is essential to accurately assess their resilience. In particular, this study performs a rigorous probabilistic analysis on the seismic resilience of a transportation network in Istanbul, Turkey. The analysis accuracy is enhanced by considering, along with the structural damage of roadways, the additional disruption mode of network performance caused by the debris falling from damaged objects in their vicinity. Moreover, we obtain the results as a map of resilience measure, which enables us to investigate the disruption inequality across the study area and identify critical factors that govern the system resilience. To enable such sophisticated probabilistic analysis, a Bayesian network (BN) model is developed that involves various types of information from the hazard process to the performance of structures and systems. Then, the BN is quantified by identifying and compiling a comprehensive list of datasets. Thereby, this study analyses large-scale systems involving thousands of structures, while providing general probabilistic models and data schema that can be employed for other transportation networks.
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