Fragility curves of restoration processes for resilience analysis

In literature the fragility c urves are usually adop ted to evaluate the probab ility of exc eedance of a given damage state, while in this paper is presented for the first time a procedure for build ing fragility curves of restoration processes which can be adopted for resilience analysis. The restoration process describes t he capacity to recover of a system after a failure. In order to ha ve a resilience system, it is necessary to reduce the consequences from failures by shortening the recovery time and reducing the probability of damage. The restoration process is one of the most uncertain variab les in the resilience analysis the refore, it is necessary to consider it in probabilistic terms. The method has been applied to the performance of a hospital during an emergency. A discrete event simulation model has been built to simulate d ifferent restoratio n processes. The set of restoratio n processes obtained through Monte Carlo simulations has been analyzed statistically to determine the probability of exceedance of a given restoration state. Restoratio n Fra gility F unctio ns (RFF) are obtained using the maximum likelihood estimation (MLE) approach. The probability of restoration for a given earthq uake intensity (e.g.MMI) level, x, can then be estimated as the fraction of records for which restoration occurs at a level lower than x. A lognormal cumulative distribution function is used to fit the data, to provide a continuous estimate of the probability of restoration as afunction of MMI. Two different case scenarios are compared: the Emergency Department (ED) with and with out emergency plan applied. Finally, different methods to build fragility curves are compared in order to evaluate the RFF.