ABSTRACT
Resilience typically refers to the ability of a system to adapt to changing conditions, withstand and rapidly recover from disruptions due to extreme/rare events. This paper proposes a methodology to predict the recovery process of a selected system given past recovery data and estimate the probability of exceeding a target value of functionality at any time. A Bayesian inference is used to update the model parameters as new data become available while the recovery process is in progress. The methodology is general and can be applied to continuous recovery processes such as those of economic or natural systems, as well as to discrete recovery process such as those of engineering systems. As an illustration, the proposed methodology is implemented considering a bridge restoration following seismic damage.
