Abstract
Natural hazards regularly cause severe damage to infrastructure systems. While these risks are highly unpredictable, the concept of resilience can be employed to make infrastructure more reliable, robust and recoverable. Infrastructure resilience is the capacity of an infrastructure system to revert to a desirable performance level following a disaster. As recovery—a time-dependent parameter—constitutes an element of resilience, this study quantifies time-varying housing infrastructure resilience against flood hazards using a dynamic Bayesian network (DBN). It develops a framework to quantify time-varying resilience and implements this framework in a real case-study area (Barak Valley, Northeast India). After selecting resilience parameters, this study develops two DBN models by considering two different approaches. Then, all of the information necessary for the various time-dependent resilience factors is collected through field surveys. The resilience values for different time periods are calculated using both approaches. Finally, this study compares the vulnerability, robustness and recovery scenarios of each area in the Barak Valley. This comparison, alongside the evaluated time-varying resilience values, will help public authorities provide resilience based decisions.
Published Version
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